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   "source": [
    "# Explaining Tree Models with Interventional Feature Perturbation Tree SHAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "Note\n",
    "    \n",
    "To enable SHAP support, you may need to run\n",
    "    \n",
    "```bash\n",
    "pip install alibi[shap]\n",
    "```\n",
    "\n",
    "</div>"
   ]
  },
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    "# shap.summary_plot currently doesn't work with matplotlib>=3.6.0,\n",
    "# see bug report: https://github.com/slundberg/shap/issues/2687\n",
    "!pip install matplotlib==3.5.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example shows how to apply interventional Tree SHAP to compute shap values exactly for an `xgboost` model fitted to the `Adult` dataset (binary classification task). Furthermore, the shap values computed by Kernel SHAP, an approximate feature attribution method, are shown to converge to the interventional Tree SHAP contributions given a sufficiently large number of model evaluations.\n",
    "\n",
    "This example will use the [xgboost](https://github.com/dmlc/xgboost) library (v1.6.1). The latest version can be installed with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
       "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "import pickle\n",
    "import shap\n",
    "shap.initjs()\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xgboost as xgb\n",
    "\n",
    "from alibi.datasets import fetch_adult\n",
    "from alibi.explainers import KernelShap, TreeShap\n",
    "from collections import defaultdict, Counter\n",
    "from functools import partial\n",
    "from itertools import product, zip_longest\n",
    "\n",
    "from scipy.special import expit\n",
    "invlogit=expit\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn.utils import resample\n",
    "\n",
    "from timeit import default_timer as timer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `fetch_adult` function returns a `Bunch` object containing features, targets, feature names and a mapping of categorical variables to numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'target_names', 'category_map'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult = fetch_adult()\n",
    "adult.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = adult.data\n",
    "target = adult.target\n",
    "target_names = adult.target_names\n",
    "feature_names = adult.feature_names\n",
    "category_map = adult.category_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for your own datasets you can use the utility function `gen_category_map` imported from `alibi.utils`  to create the category map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "data_perm = np.random.permutation(np.c_[data, target])\n",
    "data = data_perm[:,:-1]\n",
    "target = data_perm[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = 30000\n",
    "X_train,y_train = data[:idx,:], target[:idx]\n",
    "X_test, y_test = data[idx+1:,:], target[idx+1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`xgboost` wraps arrays using  `DMatrix` objects, optimised for both memory efficiency and training speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wrap(arr):\n",
    "    return np.ascontiguousarray(arr)\n",
    "\n",
    "dtrain = xgb.DMatrix(\n",
    "    wrap(X_train), \n",
    "    label=wrap(y_train), \n",
    "    feature_names=feature_names, \n",
    ")\n",
    "\n",
    "dtest = xgb.DMatrix(wrap(X_test), label=wrap(y_test), feature_names=feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, a matrix that contains the raw string values for categorical variables (used for display) is created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _decode_data(X, feature_names, category_map):\n",
    "    \"\"\"\n",
    "    Given an encoded data matrix `X` returns a matrix where the \n",
    "    categorical levels have been replaced by human readable categories.\n",
    "    \"\"\"\n",
    "    \n",
    "    X_new = np.zeros(X.shape, dtype=object)\n",
    "    for idx, name in enumerate(feature_names):\n",
    "        categories = category_map.get(idx, None)\n",
    "        if categories:\n",
    "            for j, category in enumerate(categories):\n",
    "                encoded_vals = X[:, idx] == j\n",
    "                X_new[encoded_vals, idx] = category\n",
    "        else:\n",
    "            X_new[:, idx] = X[:, idx]\n",
    "        \n",
    "    return X_new\n",
    "            \n",
    "decode_data = partial(_decode_data,\n",
    "                     feature_names=feature_names,\n",
    "                     category_map=category_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_display = decode_data(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[52, 'Private', 'Associates', ..., 0, 60, 'United-States'],\n",
       "       [21, 'Private', 'High School grad', ..., 0, 20, 'United-States'],\n",
       "       [43, 'Private', 'Dropout', ..., 0, 50, 'United-States'],\n",
       "       ...,\n",
       "       [23, 'Private', 'High School grad', ..., 0, 40, 'United-States'],\n",
       "       [45, 'Local-gov', 'Doctorate', ..., 0, 45, 'United-States'],\n",
       "       [25, 'Private', 'High School grad', ..., 0, 48, 'United-States']],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model fitted in the `xgboost` fitting [example](xgboost_model_fitting_adult.ipynb) will be explained. The confusion matrix of this model is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_conf_matrix(y_test, y_pred, class_names):\n",
    "    \"\"\"\n",
    "    Plots confusion matrix. Taken from:\n",
    "    http://queirozf.com/entries/visualizing-machine-learning-models-examples-with-scikit-learn-and-matplotlib\n",
    "    \"\"\"\n",
    "    \n",
    "    matrix = confusion_matrix(y_test,y_pred)\n",
    "\n",
    "\n",
    "    # place labels at the top\n",
    "    plt.gca().xaxis.tick_top()\n",
    "    plt.gca().xaxis.set_label_position('top')\n",
    "\n",
    "    # plot the matrix per se\n",
    "    plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "\n",
    "    # plot colorbar to the right\n",
    "    plt.colorbar()\n",
    "\n",
    "    fmt = 'd'\n",
    "\n",
    "    # write the number of predictions in each bucket\n",
    "    thresh = matrix.max() / 2.\n",
    "    for i, j in product(range(matrix.shape[0]), range(matrix.shape[1])):\n",
    "\n",
    "        # if background is dark, use a white number, and vice-versa\n",
    "        plt.text(j, i, format(matrix[i, j], fmt),\n",
    "             horizontalalignment=\"center\",\n",
    "             color=\"white\" if matrix[i, j] > thresh else \"black\")\n",
    "\n",
    "    tick_marks = np.arange(len(class_names))\n",
    "    plt.xticks(tick_marks, class_names, rotation=45)\n",
    "    plt.yticks(tick_marks, class_names)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label',size=14)\n",
    "    plt.xlabel('Predicted label',size=14)\n",
    "    plt.show()\n",
    "\n",
    "def predict(xgb_model, dataset, proba=False, threshold=0.5):\n",
    "    \"\"\"\n",
    "    Predicts labels given a xgboost model that outputs raw logits. \n",
    "    \"\"\"\n",
    "    \n",
    "    y_pred = model.predict(dataset)  # raw logits are predicted\n",
    "    y_pred_proba = invlogit(y_pred) \n",
    "    if proba:\n",
    "        return y_pred_proba\n",
    "    y_pred_class = np.zeros_like(y_pred)\n",
    "    y_pred_class[y_pred_proba >= threshold] = 1  # assign a label \n",
    "    \n",
    "    return y_pred_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = xgb.Booster()  \n",
    "model.load_model('assets/adult_xgb.mdl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train = predict(model, dtrain)\n",
    "y_pred_test = predict(model, dtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conf_matrix(y_test, y_pred_test, target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  87.75  %.\n",
      "Test  accuracy:  86.3672%.\n"
     ]
    }
   ],
   "source": [
    "print(f'Train accuracy:  {round(100*accuracy_score(y_train, y_pred_train), 4)}  %.')\n",
    "print(f'Test  accuracy:  {round(100*accuracy_score(y_test, y_pred_test), 4)}%.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explaining xgboost with interventional Tree SHAP: global knowledge from local explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that the goal of shap values computation for an instance $x$ is to attribute the difference $f(x) - \\mathbb{E}_{\\mathcal{D}}[f(x)]$ to $M$ input features. Here $\\mathcal{D}$ represents the background data. Unlike the [path-dependent perturbation](path_dependent_tree_shap_adult_xgb.ipynb) algorithm which exploits the tree structure and cover information (derived from the training data) to obviate the need for a background dataset, the interventional perturbation algorithm follows a similar idea to [Kernel SHAP](https://docs.seldon.io/projects/alibi/en/stable/methods/KernelSHAP.html) and uses a background dataset to compute the expected value as the average of the leaves where the background samples fall plus the baseline model offset <sup>[(1)](#Footnotes) </sup> . As explained in the algorithm [overview](https://docs.seldon.io/projects/alibi/en/stable/methods/TreeSHAP.html), this allows explaining nonlinear transformations of the model output, so this method can be used to explain loss function fluctuations.\n",
    "\n",
    "As discussed in [[1]](#References) and detailed in the [overview](https://docs.seldon.io/projects/alibi/en/stable/methods/TreeSHAP.html), this perturbation method enforces the conditional independence $x_{S} \\perp x_{\\bar{S}}$ where $\\bar{S}$ is a subset of missing features. This section shows that this method is consistent with the path-dependent perturbation method, in the sense that it leads to very similar analysis conclusions assuming an appropriate background dataset is used.\n",
    "<a id='source_3'></a>\n",
    "<a id='f_3'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the background dataset contains $30, 000$ examples, the next part of the example would be in principle a **long running**. In practice, sufficient accuracy can be achieved using a couple of hundred samples (the library authors recommend anywhere between 100 and 1000 examples), provided that the samples chosen represent the underlying distribution accurately (i.e., they cover the entire support of the distribution). You can skip the computation by setting `COMPUTE_SHAP = False` and can **load the results** by calling the `load_shap_values` function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Warning\n",
    "    \n",
    "The upstream implementation of interventional `TreeShap` supports only up to 100 samples in the background dataset. A larger background dataset will be sampled with replacement to 100 instances. Some of the issues related to this limitation have been reported [here](https://github.com/slundberg/shap/issues/2487) and [here](https://github.com/slundberg/shap/issues/1991). Thus, we will use only 100 background samples for the following experiments.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Predictor returned a scalar value. Ensure the output represents a probability or decision score as opposed to a classification label!\n"
     ]
    }
   ],
   "source": [
    "background_indices = np.random.choice(len(X_train), size=100, replace=False)\n",
    "background_data = X_train[background_indices]\n",
    "\n",
    "tree_explainer_interventional = TreeShap(model, model_output='raw', task='classification')  \n",
    "tree_explainer_interventional.fit(background_data=background_data)\n",
    "\n",
    "COMPUTE_SHAP = False  # whether to compute the SHAP values from scratch (the computation is quite fast).\n",
    "\n",
    "if COMPUTE_SHAP:\n",
    "    explanation = tree_explainer_interventional.explain(X_test)\n",
    "\n",
    "    with open('assets/shap_interv.pkl', 'wb') as f:\n",
    "        pickle.dump(explanation.shap_values[0], f)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_shap_values():\n",
    "    with open('assets/shap_interv.pkl', 'rb') as f:\n",
    "        shap_interventional = pickle.load(f)\n",
    "    \n",
    "    return shap_interventional "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "interventional_shap_values = load_shap_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the local accuracy property holds for all examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0.0: 2560})\n"
     ]
    }
   ],
   "source": [
    "errs = np.abs(model.predict(dtest) - tree_explainer_interventional.expected_value - interventional_shap_values.sum(1))\n",
    "print(Counter(np.round(errs, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(interventional_shap_values, X_test, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 1: Summary plot of the interventional perturbation Tree SHAP explanations for the test set \n",
    "<a id='figure_8'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(interventional_shap_values, X_test, feature_names, plot_type='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 2: Most important features as predicted by the interventional perturbation Tree SHAP algorithm\n",
    "<a id='figure_9'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One might be tempted to proceed to compare the feature rankings displayed above with the ranking provided by the path-dependent Tree SHAP [example](path_dependent_tree_shap_adult_xgb.ipynb). However, these algorithms have different ways of estimating the effect of missing features and:\n",
    "\n",
    "1. The length of the bar represents the average magnitude of the points in the summary plot above; each point is the average of the shap values computed for a given instance $x$ with respect to $R$ different background samples. Hence, one can consider that for each instance to be explained the shap value of the $j$th feature is a random variable, denoted by $\\Phi_{i,j}$. One way to define the importance of the $j$th feature, $I_j$, is \n",
    "\n",
    "$$\n",
    "I_j = \\frac{1}{N} \\sum \\limits_{i=1}^N |\\mathbb{E}[\\Phi_{i, j}]|,\n",
    "$$\n",
    "\n",
    "where the expectation is taken over the background distribution and $N$ is the number of instances explained. This corresponds to the notion of feature importance according to which a feature is important for explaining the model behaviour over a given dataset if:\n",
    "\n",
    "-  either the instances to explained are consistently affected by the feature, or the feature has a particularly large impact for certain subgroups and a small or moderate impact for the remainder. Traditional global explanation feature importances hide this information whereas the summary plot reveals why a particular feature was deemed important\n",
    "\n",
    "- locally, one also requires that cancellation effects are not significant. In other words, for a particular instance, a feature would be considered as not important if, across different backgrounds, cancellation effects result in a small  average for the effect.\n",
    "\n",
    "It should be noted that the error $I_j$ is inversely proportional to the square root of the size of the background dataset for a given dataset to be explained, so it is important to select a sufficient number of background samples in order to reduce the error of this estimate. \n",
    "\n",
    "2. The two methods explain the dataset with respect to different expected values, so the contributions will be different. This also arises because of the different set of conditional assumptions are made when estimating the individual contributions, as explained in the algorithm [overview](https://docs.seldon.io/projects/alibi/en/stable/methods/TreeSHAP.html).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of analysing feature importance rankings, it is perhaps more instructive to look at the dependence plots and see if the conclusions from the previous model interpretation hold. Although the decision plots in Figure 3 show the same patterns as their counterparts in the path-dependent [example](path_dependent_tree_shap_adult_xgb.ipynb), different variables are found to have the strongest interaction with the variables of interest so the colouring of the plot is different. This is expected since the different conditional independence assumptions give rise to different magnitudes for the shap values, and therefore the estimations for the Pearson coefficients will be affected.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _dependence_plot(features, shap_values, dataset, feature_names, category_map, display_features=None, **kwargs):\n",
    "    \"\"\" \n",
    "    Plots dependence plots of specified features in a grid.\n",
    "    \n",
    "    features: List[str], List[Tuple[str, str]]\n",
    "        Names of features to be plotted. If List[str], then shap \n",
    "        values are plotted as a function of feature value, coloured \n",
    "        by the value of the feature determined to have the strongest\n",
    "        interaction (empirically). If List[Tuple[str, str]], shap\n",
    "        interaction values are plotted.\n",
    "    display_features: np.ndarray, N x F\n",
    "        Same as dataset, but contains human readable values\n",
    "        for categorical levels as opposed to numerical values\n",
    "    \"\"\"\n",
    "    \n",
    "    def _set_fonts(fig, ax, fonts=None, set_cbar=False):\n",
    "        \"\"\"\n",
    "        Sets fonts for axis labels and colobar.\n",
    "        \"\"\"\n",
    "\n",
    "        ax.xaxis.label.set_size(xlabelfontsize)\n",
    "        ax.yaxis.label.set_size(ylabelfontsize)\n",
    "        ax.tick_params(axis='x', labelsize=xtickfontsize)\n",
    "        ax.tick_params(axis='y', labelsize=ytickfontsize)\n",
    "        if set_cbar:\n",
    "            fig.axes[-1].tick_params(labelsize=cbartickfontsize)\n",
    "            fig.axes[-1].tick_params(labelrotation=cbartickrotation)\n",
    "            fig.axes[-1].yaxis.label.set_size(cbarlabelfontsize)\n",
    "\n",
    "    # parse plotting args\n",
    "    figsize = kwargs.get(\"figsize\", (15, 10))\n",
    "    nrows = kwargs.get('nrows', len(features))\n",
    "    ncols = kwargs.get('ncols', 1)\n",
    "    xlabelfontsize = kwargs.get('xlabelfontsize', 14)\n",
    "    xtickfontsize = kwargs.get('xtickfontsize', 11)\n",
    "    ylabelfontsize = kwargs.get('ylabelfontsize', 14)\n",
    "    ytickfontsize = kwargs.get('ytickfontsize', 11)\n",
    "    cbartickfontsize = kwargs.get('cbartickfontsize', 14)\n",
    "    cbartickrotation = kwargs.get('cbartickrotation', 10)\n",
    "    cbarlabelfontsize = kwargs.get('cbarlabelfontsize', 14)\n",
    "    rotation_orig = kwargs.get('xticklabelrotation', 25)\n",
    "    \n",
    "    alpha = kwargs.get(\"alpha\", 1)\n",
    "    x_jitter_orig = kwargs.get(\"x_jitter\", 0.8)\n",
    "    grouped_features = list(zip_longest(*[iter(features)] * ncols))\n",
    "    \n",
    "    \n",
    "    fig, axes = plt.subplots(nrows, ncols,  figsize=figsize)\n",
    "    if nrows == len(features):\n",
    "        axes = list(zip_longest(*[iter(axes)] * 1))\n",
    "\n",
    "\n",
    "    for i, (row, group) in enumerate(zip(axes, grouped_features), start=1):\n",
    "        # plot each feature or interaction in a subplot\n",
    "        for ax, feature in zip(row, group):\n",
    "            # set x-axis ticks and labels and x-jitter for categorical variables\n",
    "            if not feature:\n",
    "                continue\n",
    "            if isinstance(feature, list) or isinstance(feature, tuple):\n",
    "                feature_index = feature_names.index(feature[0])\n",
    "            else:\n",
    "                feature_index = feature_names.index(feature)\n",
    "            if feature_index in category_map:\n",
    "                ax.set_xticks(np.arange(len(category_map[feature_index])))\n",
    "                if i == nrows:\n",
    "                    rotation = 90\n",
    "                else:\n",
    "                    rotation = rotation_orig\n",
    "                ax.set_xticklabels(category_map[feature_index], rotation=rotation, fontsize=22)\n",
    "                x_jitter = x_jitter_orig\n",
    "            else:\n",
    "                x_jitter = 0\n",
    "            \n",
    "            shap.dependence_plot(feature, \n",
    "                                 shap_values,\n",
    "                                 dataset,\n",
    "                                 feature_names=feature_names,\n",
    "                                 display_features=display_features,\n",
    "                                 interaction_index='auto',\n",
    "                                 ax=ax,\n",
    "                                 show=False,\n",
    "                                 x_jitter=x_jitter,\n",
    "                                 alpha=alpha\n",
    "                                )\n",
    "            if i!= nrows:\n",
    "                ax.tick_params('x', labelrotation=rotation_orig)\n",
    "            _set_fonts(fig, ax, set_cbar=True)\n",
    "    \n",
    "plot_dependence = partial(\n",
    "    _dependence_plot, \n",
    "    feature_names=feature_names,\n",
    "    category_map=category_map,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "Warning\n",
    "    \n",
    "For the following plots to run the `matplotlib` version needs to be `<3.5.0`. This is because of an upstream issue of how the `shap.dependence_plot` function is handled in the `shap` library. An issue tracking it can be found [here](https://github.com/slundberg/shap/issues/2273).\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SYZR58bE1ZhuUTlaIdUXJ7Lr8ZsBCbEQy7xTnkwDpdaaZbzH6vXGcqkP3HZ10r9H4yG24nPzqCM2ZOrXnF+gcjJPoiND1+V3YY1XsyVqwv1Nl6ocKJG5dvcGSM99Ae5pwb3ztQb08DpUG3DAIiejq4352GgAzYmIcK+Lt6gDbJZR4Y662thbt5eAoQHOh+YbsVwghxPVEJqpi4xseHjYIgqPfXeWxB4EHAXp7e9/kkQmx/pmG4hd2KX5wEkImPLDz4gFE19fkZsDTMNwDEUvRkTD4wp1hTiz49KUVOzrXPp9pzDWZfTqolFGfbjD71BybP7Dpit+HUopKT5KfTCgM4AO9wfvQDYfmXz6PP1vF6IwT/fXbUYnwqvtoFW1Off0Mju1hKsXmD2yiTbrKC3EOmXeKlSRAep2ZfnyWViHoKzXz5ByZ3Rki2QsPeo3ZJk7FwSsFgcN60SHRFqZ1uowRPe8gbq0+MSg/OUPpkQkAknf30PaBzRdu9I9H4CeHgtvPnYB/+n4IXfixMdqieLM1wpkQmVs7cfNFwrNlwrqK2xXCGryyIt3x/jjhTBi7ZKMMRXa3FP0WQoiNRmpBibeILwNV4D+e/0Aul/sK8BWAz33uc/IrIcRluLnH4ObXuYDtm8c1L+eDX7VjBcU/uTEImPSmDXrTr93PWJ/Xqf38+1fiqcF+ykYTpeCZzTFuB9yXZvBng2xSf6GO+8IUofuGVn1+fbbBS9MeCw1N3ILomdpFA6T1mQaFw4uE02E6b++47mueCnGl5CArzicB0nVAa03hUJHWYovs7gzx3hjRjghG2MBMB53rIwkTlCKyJUV4MElrooY9ViW6K0P8hrZV91t9evbs7WfnyL5vAGWcdyA8OXP2dr4Ci1XovvDAmv7Ubuo/GQMgc0sX9b95CQbi6JZL67FRrM/fdEU/AytqsuOz26iN14i0R96wuqZCCCGuJ3IyJja24eHhPwZ2Aj+fy+X8az0eId5Kik3NVBU2pSATCY43Z8pnHx+vgK81xiWUJov3xui4tZ3Ci4uEMyF67ll99d+l0lrTbPgkq01Qilo9OPdR8ZXL98+/f66ZWIx5z0DhUXfheCzBrjW2desuI98YxWt5AHgtj777r7xMmhDXN5l3ipUkQHqd6bu/B3uxhVN16b6zk0g2zPyBPNM/DQKVhUOL7Pr8DsLpENs/MUTxWBnjvm6iJkQ2J4luC2rLdH5y+2u+lpmN4FUdAKxM+MLgKMBQN4wtNVjNJqAtufq+2qKkPh4ccv1KC2UotOODoVDR4GPmtTxmn5nHa3h03tZBrHv15foQ1FdtLdrEuqOYkSAj1oqaZHam8cst7JdmMXsSmD2rj0cIIcT6o88UrvUQhLhqhoeH/wC4HXggl8u1rvV4hFhPmgtNFg4WCCVDdA13YKyxQm4tczXNnx/WtFxN1FJ88UbojCu2tBo8+3QJtGbfLWkMlbrksW16Zx/97+i97J4Pnq95+LRmqqrZ16m4Z8BAKcWuiTzHp2xAsTvhAr2YN/QQmq7gnSpgDmUxb+lbc7/RdIjSu7YSmq3iZaJEtyXW3NYuO8vBUYDmvJQzExufzDvF+SRAep2JdkTY/es7V3yvMdtYvu3bPq1Ci3A6RKwnBobCrbkkNicueRlExye2UfrRBNrVZN7Vv/pG77wB2pNBDdKbh1ZdXn8+IxXByaZoPnQKIxul/VdvBmDyJ9MUj5YAKI9U2PvFXRihCyc3tckah/79yzhVl8yuNPu+tBsrFryuX2lR+9Pn8WvBUvv4r9yItW31DFkhhBDrzNDq9bKFWO+Gh4f3A78PHAeeGh4eBhjJ5XK/eE0HJsQ64LU8Tv+3Udy6C4Bbc9n07rUDg6s5tggtN1hQ23Q1J4qKzjjc+f3DWEcbeChuGTXQ77vvsgKdV9K5/ZlJzbNTQUL5eFnTm1QMZaDf9GjfZKAUJPCWX+f0bTs4tVUzlIX9xtqB4q1Zxdv2RHipI0JvAu7btPYYox0Rol3RIDCqILvnMsuZvTgC43nY1R98CXE9k3mnOI8ESNeBzI40xWMl0BBKh4j1xgAoHF5k4pEp0JDYnGDbx7esngW6BisTpuPj2y6+kVJwy9ZLGq89WaVVcFB3DqKB+oEFItuytPJnkyW8hodbdwlnLqyvevobo1RGgto6rYJN38/10nVr0LHeO1PCr9lAUOPHeSUvAVIhhBBCXNdyudwRZC2fEJfFqbnLwVGAxmVkN/bEoTxv06x6xFImPfuDlWytV4rsLgXnKE7dRGt9RcHOy1G1ITRTxSw2cXqTVO04Shn0bE8wczI4J+rdEWR/jhQ1X33BgYU6z7VH+ZU7IuxsX3u879li8J4trz0GI2Sw/ZNDVMdqhFIh4kvnm5fkyBh869ng9oFT8IV3w8DVDUCNFn1KTdjVoYiG3pj/N+35FL4zSmu0QmxnhuwDg2/6Z0IIcW1IgHQdyOxKsyO1ldaiTXJLEmupCVPhcHG5snBtvIZdsom0XfvanOq8rNBX77ff2Mbkj4Nu96mhJKH06jVzzu1Wr31/RWas0ZNAmQbaC66ymv2yxF4IITYKLfEjIYQQ54lkw8T7YtSng1V1bXsvnt3YXGgy+8w8Rsig995uQskQbeUGN+aLzHgm/S2PTKUNsjESN3dQfnIGrTXxG9pRSqG15nsvtHjpeJOB3hCfvi9OxLp6x6c99QqjPz6NW3OIdUYZumc3EGXn29ro3hZHGYpMT3CONzbWJPQ3RzFrNl4sxJmOPexsX3vp/KUwI0E5s8s2vUizCY06JJKa8GzpqgZIc5M+f38syKztTip+43aT0BvQWKqaW6D+Un7p9jzhwSSJmzqueL/i+iPzTnE+CZCuE/G+OPG++IrvRdsj1KfqAJhREyt+ffx3hrrjpN+7mdpzc1jtEVLvHgCg4+Z24n1x3IZLcnNizStxA+/vpzZZx6k4ZHalV3RbNLsTxD93I87RBcy+JOFbet+U9ySEEOLNIBNVIYQQ4Ht6OUlCGYptvzRE8ViJcDZMcmDtgKDWmpFvjeFUgj4Ldtlh+yeGaJRdtlvBF0CjHPwb+7l+5g+V0L6m8+f6UErx8kiLx364gPY0+RPQa3l84L4rCBy+BvtQni35Eq6rCTsN6qfKpLqitBZtSgfmUaYidm8P4XSI5MEZwjUbDwg1HFLPT8Pwjqs2tktRb+/g1HET7fqYMYudXR1cuFbwjXNk7myfu7mqZqEOfZdeQvYC2vaY902mtcWAcmizpZ/exiXzTrHS9RFRE5el7x29mDETt+bScUv7cjOjq8FtenhNj0h29cOcN1XBfnEWoz1G+M5+kvf2kbz3wtpA5zdm0i0X93gBlQpjDQWB0PZ9bdzyP95IY65BY7bJ3DPzdA13Yi5lzlpD2eVthRBCbBz6tTcRQghxnXN9jfU6yn5N/GiKyR9Pk9gUY88Xd2FFLRrzTY78pxNU51tse18vg+8LEi1mnppj4UAeM2Iy9NFBEpviq+5Tu3o5OApgl4LSXF1b45x+eJrmbJ14f5zOLUF9zMUXF6E3DhqKR0r0/IKmPlUnMVkittjAjoeojVlAECAttTQNN1iyfznLrmeqmqcnNIkw/NygImIpnJpN4/kFVMPBaY/i+8HR8My3xpbHbxdtdnx6Gz39EfYZTaraIKF8+jZd+9WDryrVQujbtkO1gZdJUClqOgav3uv1pxQji8HPKh6C7Nr9fy/J4s4O/vZxB7viEsuG+O+2ZZE1ixuTzDvF+SRAuo6Ujpcpj1RI9Mdpv7ENM2zQd3/Pqtt6M1Xc43nM/hTWjva1dzpfgnoLNnfCGkW+K2eqjH5vHN/xye7NMPjBgRWP+5UWtb96Cd1aqg1ke0Tuf+2joXZ9an/xIt5MUFsn+sEdRO7aBECsK8r4Q5PLHRTrs022ffx1FNARQgixjsmVfCGEWK9cX/O1VzQnFzW9CcXn9ikS4dX/rhdPljj0vxyAhQb5iInX8rj5d2/kmX/3CgcfL4APrxys8tn9WcKJMAsHgiXPXstj5qk5tn9iaNX9GiGDWG+UiYenUKZi52eCfgv2TJ1Uq0kIj2ijiT3fIJpMUTi0yPzTc2gN2Ruy7DEUWxM+245N06j7WCHFvncEGasvzvn8yfOalqd59xbF5264tOQUx9P89SGfuhOEZaq2wcf2KGa/N4ZRaqJRmNM1Fh6fofee7hWB3lYxCJQOvKefM2calA9XSO9JsuWBzZc0hqsp1h2FeCT4UhcmxrzR3r3NIBVRlJqa2/oNYm9QDdKRqiJ2exdRV6MsxZmKoucip9NiPZN5p1hJAqTXGd1wwDJQoZUH3OpEjdHvj4OGxSNFjLBBdvfq9Xe8hTq1Pz+IdoKaLPFf3k9o7yr1X54/Bd/LgdZBl8FP3x80ZTrP/M8W8J1gaUHxaInuu7qItp+9WunnG2eDowTZpK+HX2gsB0cB3JfnlwOk2tc0F84WYG/MNV7XPoUQQgghhBBvviMLcHIpo2+mpnluBt45uHoAIv/0POZkFaU1NF2Kj0zB797I6VyR9FwVw/VpJsOMPpln9wObUIZCL2VWmuG1O7drX2MvOqS3pVCGWq5bWhmrUXhpEbfu0khY1O6tkd6aYupnc3gFG9DMvVQIzkEKNn12C6/cwkhY+OUgUPnnL/ocmtV4GmYqmg9tV3TEDJr5FsVXguX/7fvXXuVWc6DW9Gkt2hghg4VksDKvNVLFW0pU8VFUns+jlCK5NcmJH0yigL2fCBJFJsuan+wewt/pc0IZbClqhrJrB3nq03VKJ8pEu6K07T07tpcXNBUHbuhgzSD2pcruzqB9TX2qQWpb8vIaPV0Cw1DcvfmND3ANZIP/C2UpDKXYlFn78yaE2FgkQHodaf54hNbjY6iQSeyT+wjtbGfxdIXyaA3D81bkgDcXWrB79f04r+RxTuRREQujN4F7prh6gDR3KgiOAhyfglIdshfW9Dm3tqky1QVL+c2+JEZ7DL/QQClFaH/X63q/RiaCkQgvd6U3+88WjVGGIjmYJP9igVDCouMWuWwnhBAbnRTLF0KI9StkBKcrLQ/CBoTOXWY/XYCmA1u6wDDIbEmgQga27aMM6BoMlswna03spgNakyx7JNtChBIWA+/rZ+7ZeaxkiP53rN2DQPsaz/YIJYNmsF4zSBhxqjb1qTq+6+OUDOyas/R9FzcSBMDMpofbdKFq48438Jo+quWh80GQ9fSci3phgZjtMrW1HdtN4NZdTv3tyPLruHWX7jtWb0yUDmnSJ/OcnnXRhmJLLAa0E+qIUJ9poDRoBbGtwTnRM5OaxwY2o5Tig2MuA8BY3mXvQzmy03nK3W2M9w0zlF19mX2z0OTYvzqAN1FFpcJs+Zc30XlnFz8d1zw6FiS/PDet+M1beF0lEV6Ptr3ZFYHY9WhHp8lnbgszWvDZ0WksB0zFxiPzTnE+CZBeJ/yaTevxMdyaS/3kHOVj8/Ab93DiG2M0Z4PAY/v2JNG2CEbYWLXDoO9pFk5V8b97EmO8HNTgmWmQ37sZ8+EpIjGTrjs6sZZqedKRgqlCcDsWhngY39PMPTuPvWjTdkOW1JYk/e/sRWuNU3XpGu4glFj5sVERi+Rv3Ip7chGjPYq5KU1tss7EI1OgNf3v7CM1tLJyi/Y1uuUR+9WbcF+YQaUjhJeyRwFaiy1q0zUacw2qLY+e+7vRWl9WrR8hhBDrhfyNF0KI9Wp7VjNd1RxegME0/PPbNKDgmWPw0AvBRrs3wafvJ31jO95giuZkHTNqkn1PcB6QjiuKbhBsNJQimgjOW9r2ZVc0bl2LYRn03tvDzJOzKEPRu1SOTDuaaFcU3/YwwyZ6afGbZxg4S8cejSYUD+GWbeymh+2B6WuchRYAXUdmSU2U0MCmcg27tYtm014OjkKQsfmq+eMVmmWHzh1JYtkwbtVl9+EJCrUwCe3THQ/Be9pxuuK4VjmILpsKty2K7/s8WQmRrNTRhuKJYoyPANvHpqhP5fHRZGYX2TE6CTu3rfqzKOfyeOPBaj1dtln80SSdd3Zxsng26ybf0BSbis7VS7oC4Ls+XsvHipuXfS6WbwSv2RFbH8f5XV0mu7ouLKFQtTUaSL1BWbcX8+ykz2OjmkQIPrnPoDuxPn5264v8TMVKEiC9Tl74NH4AACAASURBVCjLQFkGtdwYifEz6KhF/g8KNDPbQSm01oSjivZuk2rZZ/bpOTa9uw8rYTH/szz1mTozBY1baZI5XiG5qZ1W0WE+Emf+uSqGXWJoKExjpsG2V2v2PHB7EBitteDePRAOMffkHHPPzgNQOlVm96/uIJwJs+XDF69vo2IhQjd2L98f/+EE9tJylLEfTrD/N/csP+bXHep/8SLefA2zJ0ni125CxUIr9lc6XqZwaJHFw0W0rzn6/x3DtEy671z9iqwQQoj1T4rlCyHE+vXcNEy8WKRjqkq5PcYj2zr49H4Dnj99dqNjk1BtUDhWQc0v0lfOY7ciTOfmGfrkNvz5GqbjgQLT0bjlYKVZa7xK5elZzGSIzLs3YURMfE9z+MtHKZ8sM/DePoY+EixD776zk/ab2lCGWl6On92boTZVw150iHREyOwJsjTdkEm8WsFAU4ulcBoOc6crOI5PSHt4GEy8XOJWIF5robQHvka5Glousc4ooXQIZ+m8J72U/TlxIM/Br5zEqbm07c5w7+/twXE1X9NtjMTDWGgotLgV8MMmTsQCHzBBhwwMw2DLYhn3VBGAWDw4B2pLKBJhmKlCd0LTkVw7wBPriaIshdP0MAy13NhqUxIeHdPYHuzrUGQu0uepudDk9DdGcWsu6e0ptvz8ZtQlZpuem7H67i0G9w+8MUGpYlPzzROaUgvu3aS4s+/qBruem9b8cESjtea9Qwb3brp6r1dpaR46pdEETcEeOuXz+ZuuXkPmtyqZd4rzSYD0Gjo3I1JFLKIf30PoOzl01MLrTpGoV4gmmjRDMcKeR/qlCRqPtzBCFpW7tzBlKFJDSWaemMX3NeMnW7TtSZNMRZlswSupThrZBKUiWJgU50z2W02WrzFGw/Ch21eMqVVo4dkebt3FSoSwyw7hzOqd6y/G9zSq1MCo2+je1Ir36rwwgzdfA8CbrWK/OEvk7pWNn8KZMI3pxnKtIafmrrgiK4QQYiOSK/lCCHEt2aWgPua5JbZer7HHZ4m9UABfw0yViXAd9m+FzjT+TAmtwcxEIRqm/MIsnRPjNFSEeKOM/cgrwF24o1Us00CjMHyf/FOz9L+jj/m/OYE9XcOImGjbp/2jQxz+8suc+tsRABYOFkgMJOi6vZOxw2We+9okZtjg7b8+SMfmGKf+7gzNuRba1TRmGpz5xihtv5+luzxNb20GBcw7HZhhk+lvn8ZUHlqBgU/rRNAganvMxR6bB63xepJk2iKYUYMdn95G+WSZcDZMakuwau7410Ypnwr6MjTnm+Qf6KPVn+KoirBjPE8jHOLRdJb/GUhvilGwjCDwahlk+2Norbk763F8MIaBYl8mSHk91NvPkeg0ofkyhWwSs2+Au5Z+/k7Fwfc0kWxw7hbfm8XY00bj0CKRriiZd/YH43GDrMTQ0spxT8PKVJWz5nN53Frw2uVTFWoTdZKDF5Zku5jHJ86GoZ6Y1G9YgPThM5qxcrDvH47AzjZoi169ecSjY0Fw9NXbVzNAqgkyms+9L64GmXeKlSRAeo08P6v5wWlNyND80m7F9qwivLeL6CdvxP7RUQDa9mbY87H9zLxUJlOukir5FIstlO1izFVxG1maiy282Sq65RE2wniOpnL/DuZmbEwrjN1QLE67ZHFo+AZT0YsXy44PxDn51dP4jk+kLYIZvbwrVb0DYfLfGQVfkza7gmJE0eDjpmLnLdGPX3hIzu7J0DncyexTcyhDEW2PkNqaumC78+mmA2ETZUitGCGEWG/kBEAIIa6dqZ/OsHAgjzIUA+/vp21vFu37+D98GT22iNrZhfmeYFXYzJzNf/tJGYCPvSNNf2+YwQPj7BmrUUjGSTZa7HHmga1UbtnHiYdruHWXwc/uodcyaR5dYDTWh2uEUNqnbT6o86lQWJ5GLYWH5p6Ywa04jH93nNpMIxibrWn/6BDF4yW8hofnaqywQeFwkY5b23n0XxwgenQB11A8/Moin/6vdzP77DyORxAP8WD2uWDFXFdzgZAfZKl2NhfQjkek1cLTHj4mCk3EDZbY75oqsOC0wNeEKprQYhPi8aBfws0r+yUo52wDW6U1uunRqHp86GfHaSvX8ZVi2u4G+rFSYUIJC9/1MSMmKmKhlKKrK4yaqqCUoqMnCLwWT1QoLFgQboNFxeLRMmzpIP/SIpM/ngINXcOd9L29h+poDXNTko5NwXMXj5dJDCYpNBV958Q4q/byadoFlKUon67gNlxi3THMyKWfY2UisFTGlfQ5eTcHJ12Oz/sMthncveXSwxJLPYSBIPHI9a9usCsZhsbSf2tirYjyGyQdUbx3q8Gjo5pkGN63Vc5trwaZd4rzSYD0GvB8zfdPazxf4/rwg9PwO7cppqoa9YW30zmUQlUaWO/YQ2JbL33vBufIPPW/e5lod5TmXBOVidJ9Zxfei9P4x/N4nqYvHiLytg5i/XHSd/RwaqSFuehQ0TaD/QlCdZtw3aZwaJH2G9sAmHlqjvmfLRBKhRj6yCC+7dN2Qxt+KyhuXhuvEeuKBgPXetUu96uJNpp039EZPMUAb7KMtT2YOIRu6cWbrVE6ME++qggfrjLQlzn7Oktu+u/3M31bB425Jr33dZPdlVnz9bTWtL7zCs7BGYxkmOjnbsbsSa65vRBCiOuRXMkXQohrwWt6LBwIMiW1r5l7diEIkObGKDw0Qr3mkzxWpK0/g7Gvjz/5VpHRiRYamCp6/ME/7SHeF2WwOMfmYhWFIrY9qBl69K9HGD/YxHN9in91ho53bqYw76N9i7Dro4EiQcQu5Nn4RnCKamhN1y0p6jN1ymNlXCdoqTJzYIHdgOF5LLghtNJYLZ/UUJxmqUXy4DSWF9QFbT06CtzNojJROni+RpNXwWvEvSYdjRIA5WgCDIOtn9nO1L/L4RsGhvaJ7AvKiIWqNmHHQ/uaSN0BFYRXfMenNlkjnI0sZ2/u+GA/xRcLOLZP25Y4nbe0EzlTYtvcInhBM6bukSkgSAzJ78ni2x5G1CK1JfhZaIJO7Si1HMnpKFSJGZqmB1FD012oAh3MPze/vM18boGee7oIJS1s26da9YlEFD2pIKp3e49ivBKcP23LKtovmj+jqDvQcBQh10eZl36c/tQexY9Gg7fx3i3B80/nPb59OChL8PKsRzykuKn/0hJz3jWomK4FdUHv7ld0xa/uHOITuxQPnQkSpN83dPa1SrNNCuNNkp1huoYuUsz1Et272eDei1e5E1dM5p1iJQmQXiPn/iqaCh4a8XlmSgMmb7/rDt41uPIqUWh/F7H6TkKjJXRviuhwH2bUpPZYnaF9MRo1n7nxFnqkSHO+zuYPRTF3RqnVw+zcF2f6eB3/cIFsv8XEI8FV0EhXhFN/O4L2NPG+GDNPzNK2Lxs0cVrKHI20LxWlee4EPHwQoiH45L0wePFO9WZfEufwHEqBCpsY51T+VoYi8r7tLJxy8OM+9ck6Y98YYdcXd6Oss+87nA69Zu3TV9UPLVD61gnMiEm8T+P84yjmJ/a/rucKIYS4Pkg3USGEuDZUyMCMmHitILBoLTVlLZ6sMDHq4vqwmNeEx6qk9sHo0Qq1SpBON1a2gR5qVZ9wy8bwNNpQNLzgb/rx746hqz4KzdQLJZyyiz1RxfA1/lLyhbmUDthrVGnWTDzDJOY26RzcQ3WyQsNV+MoABfZ4UKpr9LE50jUP0/OxwxYv/9+Hue2Ph7Fcl6jjglK4Kji3UKkwrgpimr4CK/FqKqPFdLgXjSJOE8OEgRtizIfBbmkMS7Hj/iBg6cbCeEqBAidsoSwD3/V59refZuFggVDcYvgPb6fzjm5QBou395GvaNo3W3gND8P1iToe3lIgM2EHN3b+4iCFE2VKp2v0v62L3ts6gga5ZZvkYJDw0SoGWa6bb8gw8Nh8UCPVMth8U5BAYiVDy/0fzJiJshRGKsxJL8rEXJ102mRre5CMcnO3oj8JVVsxmA6aYa1ltKr53rZBXBRdymVvxSW6RksI7fks/mAce7xKbE+WzLuCxltdccWn9658jXxtZe5evr5UgPUS9CUV/8PtmpaniIWufoZld0Lx+f0r30e1YHPo4Xl8/9X30/mGBknF1SXzTnE+CZBeA6ah+MWd8NAIhEz48Db4yyNnH39uGt41eOHzwnf0E76jf8X3rO1tWGeKGIaPZ1mYMQs0VE9WuOGBs3U98xmPiYXwch3QxlyTwpFF6pM1QuUKtVPQvjNJOB0itS2F9nza9maD7vMtB374fJBBWvWC2196/0XfY/htA6iwibdQJ3xTN0ZmZXYovsZ3fPyGg/PCDNrQlOcXMNuiqHiI6Id3YnavXt/Ga3nYZYdINowRMnCbHqM/nCQyE6zd8B2fttukiLUQQqw/MlEVQohXuXWXmSfn8G2f7rs6iXZGX/tJl8kwFVs+spnpn85gRk0G3hOcc+SjSZ6dj+K2fMIxg854khSwa77AMTfISNzRcIDt1I8uYjhesODMg8axRQDMkkO8FQTvXNNg9uA8XjJM2PawQyaG5+OEg7m7ZyiGvDOYnktNpVn0o1gnytTDFiHHBwWNpQzTcKlJ1PEwtI/jmFRPacJtEcJ4JDwblMJeCpBuslzGARQYwIAVjGfBaMdfygepkGKrq3EnmnQ2Cji+heF5cCqICDbrLjRclNa4DQvH0xQfneb44/MUTItI1cX6g0O851vv5scH6zwybWLaLodtxb58i5hhEmq5WEuxNEMF73nxVJVQNkLnbREc26e+0CTeGSW7J0PxaJDd2rY3yMZt353m3i8MMXtwke6bMnTdEHx/8wc2Mf2PM/i2T+99PSilOHa6wZnHZknNVagkwvz4hnY+uzMoWdYVV3SdF8ebn3M4dbpFPG6wb18My1KM9rThnSyD51NMxcin4qwsJnBW9bl5ageC0gXOXIPwpgSx3dlVt93dbfL4iEu5qYlaiht6L/3cbaqq+S8vQ83R3NXn88Ftb/4y9GrePic4CpUFWwKk64rMO8VKGy5AOjw8/MfAx4Eh4MZcLnd4lW3+d+C3gKmlbz2Zy+X+2Zs1RoD9nYr9nWd/IdujPgtLtVk6LrLMYeFggdkn57DiJoMf3kzs/kGMjhj+RBXj+cpy7c1o98oJVHpLklAyhFtzUaYivSNFdbxGxivj5RdRlkHipVc4OdVC+xorbpF8/zkBSqWCACnAa9T3fHZac2RBs6mzl/cOq1WvShohg977epj4q1dQrk/nljCtx0axdrZj9iZofPsYyQdvu+B5dsnm1NfP4FQcIm1htv/yVuyygxsKwZ4+QmN57HSc8Hu2XfBcIYQQQggh1ovxhyapnKkCUJusseeLuy7aQVy7Ptr2MM6p7996YozWE+MY6QixX96H2XE2eHNuE1WA6nSDSt7GsBSNQotwJszBkw6uq1AetBzF4RMtBt4O+18aYUfdRyuIRhRwB7WTJUy9VHMTcJfqikZs99XV6IRcj+poieTuDFMvlQnbLq5lUc0GUcpkqAzKRaOxjCrdaY/ZeQ/DB89UgCLUDDJXk80mfsjAU4qw76BcA111iPgedjg4zY0uLbU3LYOQ72N5Dq4ZwlwKyBbiXcSbQS3VeigJGkrTTVpmFM9SKB2ikPfpArzxCmop09UoNfAWW8yfqXE6Gg8ySwG94PMe4MzP8uwaq2D6mkoszJljbWxPaeJ1GztkobQm4vvL/7f2i7Poio3RFae20EO8M0rvfd0Ujixihk267w5W79kVm9NfG6E2Xad6pEBma4pIW7C0f+gXVmbYnDpQJD1TRvmaeLnJwhPT8Gt9q352Gg2fp5+u4i6lt2oNN98cp7cvQudt7fi2Tyhu0nbOMvZ8QxM2IRUOvuc33BX79Osr758rHVX85tsiTJd9upMGycilB6oeHdPUnGC8z05rhnv1VV9mf75MbxQrbODaPkop2gcu7yLGYlNTbsGmFFgX+R0XQlxdGy5ACnwb+PfA46+x3V/ncrnfexPGs6r60UVqL+QJdUXJvKuf23rgkVHYnFJ8fNfqfxSdmsvUo9OggyzK6cdm2PaJIQodWZ49YeF0R+h0W+y/M0PHzW0rnus7PkMfHcQu2kQ7IkQ7o7Tty9L6ew82xUlloDVVQbd5oAzcukttsk52dwYiIfiFO+CRF4Ml9g/cvub7Gilqfng6ONiPlYMC0/f0r75t9x2dJJuDtB45jQJsz0dFgsmKbqx+QF18uYhTCa74thZtSifKtO3LEu2M0KQDd0sHHe/tx0iEV32+EEKI65cUyxdCiLNaizahxSLK9bD9NnwnaOKzGne8RP2/HEY3XcK39xH7+V34hQaj3x/l2VqUtlmXd/zDaZKfuQG77HD6G2dozDXpuLmNze/fhG/7TD0ddGj3Hc3kE3NktqZoTVRoNTwMrfF8D3u+DkDdVjSjQTDI84K5uVdqYXk+vjIw0GAHwcmw20CrcJBw4bvUZpukDY8p5VOPWigFXXYQTB3YYVIr+ri+IhF1yGyPc+bpSQzt4RkGoJfri0Z0izrx5R4JcbeFVgpXKSwdnI+4S495KZOdleNEfJuWGaWeGQr2cd8AxWdmAYjua8OKWjgDGexwGO1qMMEYCLIg3WILy3cx8HHcME7dodUepWFZhFwPFBRSQaB309EZJuIJPEORrTYJPz2B8ZEtGFoRbS2d58SC/8t4voyeKqGbDqrZIl5vABke+9XHWXypCMDCC3ne+Rf3M/HIFLNPz9GquVRGLMZ+OMHOz2zHqbnMPDmLdjTdd3cR7YiwNQ0nNViexjMM+sJrH2UbDX85OApQqwY/47cPKjzfZKFhcGuPQedSAPL7p3x+NqMxVLAy8sYuRWK4i/rhRdxCM8ge3d+26mu9KhZSbOu4/FV/566qV0phXYM+RrGUxa0f7qU03STZESbZcennoMcKmq8fC/qTbE4rfnW/BEnfLDLvFOfbcAHSXC73BMDw8PC1HsqanIUm+b87Db6meRxOlhUP9/YCMFODxkwDHJfUliTGuX/59cpf4VfvHTvWxPU0KhslT5QjyTQTRzVb0nA7dca+O0Z5ok6kK0rb/iyN2Qaxnhjdd3aSfKAX79A4Omwy6maplz3iGQNlKaLnNk26ZSutLf008y0SbfE1PzhLsctl5ZZG++DZflDb9DyxezdjNBy8yQpmfwp/qkp9tona00e85V0wCQwlQxfcN0IG2z+5lcpolVAqRKJfljUIIcR6JLWghBAb3nQB/uFgENT74K3QvfoSZID2uQkqX3safE3y/u2YkRvX3Lb12ChusYXv+JCbJnz3Juotnz9ZzNKq+fghk8opxaeBiR9N8ey3p6g3fXoOl8nuzpDcnGCxYDNxYBEjpNj/QJDhUHtiinokiun7uKZB6e9H4H/aR90IwVKcr24EQaGOVoWGY6CDTkhE/eDEIOMWqBkpQBHVddr6DYyqRmmWA5nWUhai8bZtVA608DBRKZ/2WwbofKXOxPeCJBGlNdarLxy2iLutpcZLCrM7Q7Q7xmIsStx10ShaVnAu0Xl6nKRTxPQcPLNO5MQocBe3vL+Dg0fn8Hy48R1BPU+1txcr7JGuF2hF49RuCH4WyUaJLdUxFJpCuJ3GdINYqYWjNV2NJpVIiNRiEEDuqtaxag62ZZJqtDBVlrABzbiF4bn4yiCydF6TKi2yszRB0zdJ2B7h2l4826NwoLC8fHv2sSCIW3ilTHHBxkdRbzjkXy6xExj/wQTVpdqstak6e764EztikWi5qJaHNg0IrX2MzWZNOjtDLCw4GIZiaOtS3QENJxY8xss+/QmTPZ0WdUfzs5mlBlVa8/hEECC10mF6/9k+vJqLmQqtyE6+Uo6nmapCWzRIwAF4/1ZFzYGSDff2K9qi12YOEUtZxFKX3xz4wGwQHAUYL2tmaoqB1Bs1OnExMu8U59twAdJL8Knh4eH3ATPA/5bL5Z5ebaPh4eEHgQcBepeCmFfKK9lB+7slCzNNWNq1OrnIyz+ZoTMKsZ4Y2z+1FcNU+OUWVtSi7/4eZp6aIxS36H97DwCRc5YkFFpwcEJjhDRHJ13yz0+QfnYaraGZbzH90xl63taNsXSJLfur91B/upfnn6zid2aw5xqkN8XYcl830VcbNAHV8Roj3xxFe5pQOsTOz27Dil348dndBr0JxUxNky416Dg0xROH8sR743Tc1MaWn9+8YmmQMhTR921fvj/yt6epjNbwR1vM/+kxhn5xC/kX8rQKNh03tZHdnyUzVsMp26R3pElvD44eZtQMsl2FEEKsYzJRFUJsHCcXPCaKPju7TDZllpIevvYklIJAFl9/Cn77Q2s+3374aFC3X4POncGvNDFSUbSvaRVtQnELcykBoVFwWPjxJLruEN3TTjJkMldWdB6eZ2A8jxMyGcvsBuDJR+aYP1Um5Lgcm4+x+2iF3ZsTvPBYnlJdY6KpPDzHbV/YyawVJWp7hDyPljaZ0cH5gWl76KWyW8oJsg3jXhWrZWAbESzfIUFQHsBwPYabP8PQPtOhPlLdKaxMnI6/OkzIbaKVSWJPEIQcfcqllN4EWlP0DNI/myXVlybu2fh+cIyImEHgNdoXpTbZQqMwtE/Xu/pRLYeEtrEtC/j/2XvTIMmu80zvOeeuuWdtWVtXV/W+oBuNpRsEAXABuIrUShLaSEpDSaOQwiNOyJbGExMTdsRE2DEOSbZnbFMajRwaeTQjUrJoiaAoiaREAgQJkGws3Wig0ftSXXtl5Z55t3OOf9xCYyEaZJMNgCDv86duZt08d828333v+32foSxSZ+okG9hRI3WYKCiJ1Jl55Y9PUiYGCYt/cZ7p3zrM6KVLhM6AxnAFVybMXT4NHKIWrCI2LSrD0QaWiAlP1rnv0vzm1cugnFT09KZy5C4H5KMEIwW1O2t4MwXG1QaloI0WkkEtbWLkFMFr9XHbMdZkDqtg0JjUG/PcLePmvWPgeyR5D6IEHIugkBpaGgsDHrqgiI3gjpEBexJD/2IXz6QNuIwQ6KXwmuealIJDdxR49IKiVhJMTaX3eb//cMBnH2xDpHnyCZeZX64yU5F4tiBM0nUqvcA0KSyJXb6xmXyRMvzfTxlWegbHEnxkH8xWBBVP8LGDb/y4YeQFniRbCm7w7st4Rd7450/GjeWHVSD9A+B/Onr0aHz48OF3AX99+PDhfUePHq2/dMajR4/+IfCHAB/96EdviAvb3VrEnSoQLfYQjmTLXWOIgcAYQ2mxTckxgGCwMiBYD+Dhi8RPrSJ8m6EPH2Ds4/vpXOyy+PAqq3WF2FalUpBIV+JXPa7002BFdWM62qLqSHrLAxqXe0hX0ni6yfDBIfrLA6p7KnQnJtDj6abroselJcXgaItdjkW5lgZBzVMtzGbaRdSKaJ7tMHKg+qIng/XjDerH6hxZGmAfGsOc3aB7uk2wGpB0E5yCTedSl/K2az8S662FaKD1+DJuo8mzxxZgcgRv2GP+7xZY/PIyYTMi7qY1SHWsX+yyzcjIyMh4w5I9yc/IyPhB4dSq4s+eSDuPP3xB8at3utRKEnrB8zO9cPpluNjPsyxLGAQjSZ8JJCSaC395id5CH8u32P6hOXI1n3OfuUJ8soswhni5zlgnxpxusv/sIpbREEDpb07C/3YT3aMrTDe7CEANQi59fZXhfSVaPU0hjDFCsHQxXbda0EeGAkspEsvCcTY7pScGRCqMXhXxLBjv1IktB1sl9AqpWDgdLdH282ghGA7qWBbkxyym9QImMQgBfjV14EV+HqP6GAPClsS2h+g1Ge7X8awIZSRm8xZ2UC4QrSu8KKZVyLEtZyOEoJz0aDl5hDEM6dTRWd2eJ7I0RimEbVPaln/xur9g2nU1fatMpCVKmqvuVmPb5IMBwhj6dg4r71HSMeubtUQBcqT7Z+TIGM21ZVSi8SoOuakiZqXLSNxAAbZJ8PppQyMVWzjDDlQtcCx0bGM5FlbOIt4sO+aU0305fk+Ncw+uYQYJwrOYeEtqmPnbpstTQXpPdKVn8TZLsPNghfmKS9yNkY7FrlvK6fK04etL0IkMt48LRvOp2PnHT0MzkLABoTTcOSV45qkul4RH4gtK/YRTpwdsv7PIz++FL89Dzob3bnt1r90XW7Cy2fU+VoYnVlOB9Eaw3jc8vJDWUn37jCD/Ci7bV4t3zAqkgEYAhyfEVYfsy6GN4cuXDEtdw4ExwaHx7D74eyGLOzNeyg+lQHr06NHlF0x/4fDhw/PAAeDB12L50pHUfmkP0VIfY0nyjYifKYV0Cj6Vnk9wIr2QW76FNYgInloFwAQJ0VfmMT+W5+JnLrP8VJPBaoDYM0LuzinuuXeEAMGp44ZWaKiOOuxzY0zNZ+N0G4o2yggGjQgVqKtCZXncu1pcun55wPCMT68Z8+xX6tzxwfRp7nNdM+NQsXq+z4a/wdhKwk33jYI2NC91ufSZy1z54iIqULj/uIgc8lmUHsHAYsaGaqRpr4TkJvM4L5NuD1DeVmLtoStMnjyO62hEQ7DW3wfDcwzWAqycpHOui9GGhS8tIz2L6Xtfvth4RkZGRkZGRkZGxuvBpQ3F5GMXyc83ae0ZZ2Hf1lQgvfcAfPF4OtO9106ZB1gIiwgVAIYVq4zShsHlHmuPrdM42yE34lHeXmLmvdMsHu8Q5aoYwNKalT8/y3pL4yWp4CmAUqMDwNhSE5RAC4GTJDjHVhmsjFNMNDqfQxjww9RtWOwn+EGAlyQEjoOy026yRinMZvq60KlQWiw69Jw8oePiqATHTYXD9eEykZUKOV4+ZveoS+fYMuQkqcVREDRTMdl95xytM73UlbqlSP7WGkvHrrAtvIg0CgF03VRMrbehMTqGNBqFxdJTTeZyLlbBMPA9BAaZpC7WcNdWPB0hjMZoRbB3KwVg5lf2cfE/nsQY2Hr/dqQl6e/biao+i1ofIH2b3h03UwXsYgGrI5DGgPSobs1T/rldLP3pGUKd7uPxQ2nJhEBbqIKDFgJlgfEskv6AedbdNQAAIABJREFURBmUSN2tor0pkE8Pw23boTuAahHKOYwxFKZ9uhdTt3FxNhV0dx6p0v+tm1h8qsX4/jK770rrfC5GEiuf3tpvIAlDxZa3jrHvgzOsPlKnsCXHoX+aZu19/qLh60up4PjUOnz8NlgfQDN4Xi0+20wF0mLRQjVTESm2LIZL6fRsRfCL1yFSBv30HPHz119zdMgHKQR6s9zc8A1KpTfG8J+fSe+bIRUoP7z/tRfMbCl419x3ttxHFwwPXU6/V2c3YDRnmC5nIl9Gxo3ih1IgPXz48PTRo0cXNqdvIe14f+q1XAdhS+SQx5k/PU/STxBSsOcnZii+Y4K1ik3Ujhi9eRjHE4RSYDbTKkTeIe4lJMs9zPEVZC/BWmgSzZbprhRYfOAyh082cQ+Nsv+nZ/EObOfS3y7QMoJBK0bHCr/isPOjOyhuLWC0IYk0B94xSq+RkIQa25NE7Zj+lR4rx3LUDlax8xb+mE9nISDePsTppkR8o4t3YZX6U+usrMZ0EpdcoNADhYoU/dUYkw+QWnCiMsTGAgz+sk7p801+4jdmKQ0537Jfpu4ZI7e8TPSIxPIdTGKo6A69RBP3E9oXBmkH+2EPy5VErfhbxsjIyMjIyMjIyMh4PRk/doW/faTBhuMyPb/A+N1F2FKDu/dxeXYWIWFms26+jhOaH/xjgmeWcOdGGP7rX0EWPERnQGLS2zURKaJ6j96q4uGvtGjZNu5KiBlbZea90wTSQm+W0FJasHK2g1WQKCmJLCut97nZTEnEGikEtgGNILrUIGoMGOqFjM3XMVKyVk3XbbyxQWzZGASFIMSLUiPH47Mlyp20jmirLPlFIPdTN7P2B0+BgcD1GL6zBsDTt9/B7Ilj2FpxYutedu6cQnhFeoUK+U6LRNoMbrsJIC0P8Cs3oQYJ7nAOFWpMJ8CTfdCbTZqsTfE27CFFD4HGGIlrRtBhwtLQMG4YY4ArI2McBvR8nWh8CBnEaN9FXVwHoPZPDlB5xyw6TshtT8VGMQhpmTymkmMgBclqKrJ6eZtGrgbGYDkSYk3x8CT86kHEZ89jxnKMfuI+ABLHplcpYpRG+ZsNcFcUC6URRvtttLRYKNc4ADhv3oq+1EQvtrH217D2ph3r81fWqTbSZav5dJuFEOw9kGdmCPKTeeRm6bJ9I4KvhxJjDNuKBte1iEJDvNJhqLGO9op0e5r8GCz2nj9Pu5GhHQpGclB0Bd0oveec3RTd3nJHmeV+m3Zfs2XSZWb2+ns9nH+2z6ljXYSAPYeKbNtzfWOM5QX374Fja1DLw93T170KL0ukuCqOQioSf7/TfIHp3GBohobpzAWZkXHD+IETSA8fPvzvgQ+QVvX84uHDh+tHjx696fDhw58D/oejR48eBf7nw4cP3w4oIAI++kJX6WvFw08H/G2vRAHFO1SX9rkOVsll4WyPqJvQnL9CvuTgbR+n0u1gVXN479oGvoNnEkSQYAy4KMS5Oguf7jP/X85jEo11bIPGpMfW98+w5b3TbKwGxCeaRK2Qws4Syw8uY941xVc/V6e/EVGrudz87jH23zvKyS+ssn6sgUgUD/wffda2VNFFh7eLiLCveMb4xBi8fsAzTy4gii7CFRTW2wykha1iLM9GI1Ell3i2guoJBl5aUKXTUTz9tSZ3vn/sRfsjemKZ4IHTeL0BhSmfxHOwcxa1j93Ocq+IsAR2zqL5bBtvyMUf8xne4qIePIMYLyH33pgasRkZGRkZrw9ZqlNGRsYPCmdP95ntLnFzv81aZZTzJzpMHaltdv9OS1jdFWnePSdp/psvcPHxFZaLFcZONVH/3QOM/8GHcAYx2rEwCLwkItoIOfFYj5ZtQaKJpOD4xYh7AW0J9KZLU0hDe3HA2KxPx/VIZUUINv+f0xGR5WAQ2EbhtiOiZ+rMLDWRm02T3CgVU4tJRM+AEhLLaPKbDZLGVZ+D9bRx0rFCqljJw1uJZ5eR9R6m5GP9SCp6WiMVvrTvrQilKPg2Odcgt5Rp/NKPsn5yCcbKbP+JOQBGbx2hO9/DLroUZ4vkaj6Tv3aE9d/5NH6UqkOd7VsB2DHUY3BqhcDxKYVdqj97H9qxibzc1d62qpBmzLmHtxL/tY92PbAE/u0zAGw81WDhH5Yw2jBxj6J2xyhmpc3QMIShwHHA7qWK4sboCMUrbQAGfp64mGOhY3hg3176Q7PYvsVGw+Pn5iB0HVSkMMoQOxY675LfIjg3Osdqv4OWkriapryLnIP/sdtfdP4YpRitr9MRLgJDdSlNxw/WA87+u+Oo+TZyqsTOf34zufEcv3D/GFv+ZoU4gbfeN4y0BAsPXCb49CmiBKwLbS787uPU/s972D8MK0+1kEHC0P4qQ77EkoJfPgjH1wRDPtw8ll6P37nLokOV9QHcPi6YKl7/dfr0N5tEp5og4HSsrlsghVQA3jdyY2MEzxbcNCp4ej09WW4bv3HjNweazz6T0I8M9+602TV2/c7Zl+PWccHxVUGQGGp5wY6hLG76XsjizoyX8gMnkB49evTjwMdf5v33vWD6F1/TlXoZmoHhH7seHdGnoyWPiAL7J3JcfnSdznyfjSfWiZf7jO8rp0988zbDBx3GL/dpnmpj5W1qhc0u765FexBy6VPLxBsBTsFBdxP6p1q076hx8dE6vYt9PGkQjkAIuPLQCo99dpmmcBHjeXobMXKpy479RfYczBOdadDsCC7gYq70aW+p8HAxz9vidRKVYKo+qlZGrdjYxoAxBLNV3B3DDJ9axy25sBKymlhgSWZqksELfn9y1W91j/a+cAESjcz5JFMzlN41hZga4vzoFs58fhnZUNS2FpCORMg0oJN/+RiL64pkoCj96G6qP7kX+xrp+xkZGRkZ399kgWpGRsYPCiM06baWCC2L6bWLlIu7AHj6VI+dXz4NUnDivt28e67IhfmI/3roMAPbxtGan15pMQ4MZB4njhBAIH2UEVguxBo8rdFG0OukqemBZ+Oq9FdUC4EqOOTLAguDrTUGgdkssukYRYSL3hQ9C7ur5Kc8/ChESwkGnDgVSPvbRrAvtrBJ09ujN6Vi6AdPnaTUbQGwu7cBfIDa7SOsjAm0JRAFmDg8DMBt9Yv46y6RtNjeaCH6N1MaL7P7PVMsby+TLzvM3Zamppfmiuz9pV0kvQR/zEdIgTGSk9vuIL+yTGI55O5MSxNMvX+O+vwSKuzgjjiMvnMrrm/h3b6V3pkGxhLU7kgNFM4Hj9B4+Ao8eQFxZCf5nzgMwPLXVonizU7xX1tl7PAIxSPT5D95EqeXICxB9Z5UTF1pStYL49hG0bEL3BQmxPWAC4sRq3h4fcPeZ1tw6xi9RpyWILAMiYGwnVC7bYjZbS4XVkewpeHN9127wawRgtVyEbXpcBwUc9wE1B9cIPnkCaxeiM67rO+vMvORPXSreS7vHidODI1anglg+RurDKJ0rEQJ6pvtNrZdWie+tEqoYEo3EbfvBJl2gX/bzIvXI++I7zntXD21RlJPHbAqToDXvjzaYKB59JEurbZidqvLLbfmEULwod2Cw+MCz+a7En+vxQNPJ5yrp2UF/vxYzG+/XeLa3/v4kyXBbxyWNAIYL4BjZXHT90IWd2a8lB84gfSNQqLTGqND+6uEGyGV2JD0EoK1gLWja8TNENXXLHy9jmNDftjDaEP9WIPSbBGTyzHYM47MWTxplRAX2lSNACRikFCZyDN06wgnvrhG/2KHQSPCFWmHwoXTfYK1AX0lUMTIVkhbWsS7Pdo6xh71cHMWuqPRymASQ381wDzbpCsH3DJnc9Ly6O8eZWuhR/znz9I+36U/VEae79C8Y4K5O0a5zTb0+xocybZ7azz+pQbzl0ImtuU5cOfzAYExhsufvcLa8R70QkpDNnYuj3PHfrrS4eEvtOFcF3Gmh550cZsBpW1FWGxx5utturFN3I7xV08zOnDZ+eEdWG5WsDojIyMjIyMjI+N7IIzBsUBef1zZWO9AYpEPBLFr015LnYeH/svX4enN9O6VFrz7Xk7etIvuV5exjCK0LI7t28XdQCFsEzoFtBC4yQC/bBH8xSlyoc3SUIl8GHPgycsACKGxlUmbNDkSqTWlcZ/SYABGYgT4pGJqUnTwuyFm01sqJ/MUCjbaTrDi1GiQ+KmgVfiZm1j4xHG8IKKf89h//34AhnoNxGYJsKF+KpQWOnUO5pfobATkfZtyfw0YZ3JcEJ9aBm0YnrGxc+kt6Naby2y9ufwt+84pOjjF580U0YU6tAZ0nAoCg/XkAgD6rt2U/9e/xQoCouIY8tZZhBDc99t7efqvFrAcyaGf3XSKPt1idfs+2L4vXcYzadPaRiQ5u5A6bGfGUxOGM1Vh5++9k/43r+DtHsHfl4qsuXaXUApCHHJxgLqwgdw9idfpMNnqgmPDeJqm7zuGXC9AA64NxpZYvs19//4O1j59HrvqUvvw3lc4gwRPf+A25v7+aYwULH7gFu4Dgq9dwVtppyvcCQm/Og8f2cOn/tNl+g9cRCjDJ++e5n/8NzuRwzkSIXGUQgtBXE3rx3Yv96huNgJS7ZioHeOPpK5mY8yLmvDeCPSQyz/EBQTwvqHkho79cihlWFtL8H1BtZqea6dPBzSa6bIvXAyZ3uJSqzkIIdhWvfHr0I+fT92PlSFS6XlwIyi4gkLW6T4j41UhE0hfJ0bzgjunBI8u2tQ2OuybX+OL/9Cl93QTS2lsY7CNJt8KkYAcdojacVp3M1AsX+ij8Lm8f4bBQg+73yJwfKo5RSkH2z+2CzOWY+1zFwkVPORW6PkuBwoDtrT7oDRWK8YVAkOOnJtgrces1QW4guq+KkEjZFQpLm6pUnAlO853qNxepTZiM20P2HOvx9L/57J6aJLWwiLxasCXtk2xcUYwXhH82semODD5vJvz7g+9/NPC/tKA1pk2zr5R1h9aYqMZM/bWUcL/9xL94QLizy5ArGCySLAWIIKYxokmyYRLp20RRRoTS4KuRakRMVgdUNxSeI2OZEZGRkbGjSJ7kp+RkfF9w2e+AY+fh6IPH3kbTAxd18fPnBFs6bs4SqMThxOPB9wDFJ5ep9VMnWXV42kj1tGFNWrdLhPtDdYKFcaWnxNXHKrdEIwhcgXhWoBp9blpIWTfYh0BOCoVPUe7XZRxMQjyYcykF9K71Gek0yeRNsIYbCsViIajLgOVOkURMGQpxMwQSSFBqQSEQW128666inN5l1Y+T46Ekp+m4BvfIHsGDJi0lyvJmQ3sZp+hIhBGxE+v4N53gLBtGF6ogwFhF6HgXde+dHIWibCIhURgKIl0/yW/9zkcESE88Nob9D7xENV/8Q6GthW55zf3vGgMo8yLX2+Kuz1L4qzXMcrQn55Aa4MQ8PDfdbh82mH47IB3ziTkijbTXoMrg7RBVJ6Q6p4yAxLmgjpJnCC0YFhJYIKRMZe2TlPs7bxDpZKK7NFIifX79uJ5glHL4lrSu5SCiY/ezON378JIwZHdqbjp+xJtp/0phBT4ufRey3zqNKVWKmonnztP8i9nqe0u8WyliB0lKMtiZntabqC4tUDrdAsdaYqzRdxyKkYvPbTC+uN1nJLD3E9uvSqaXg+LLcX8mmLnpM1IId26b06MYeIBBjg6XuX9m/M+WzecaRq2lgSHajfm+q+14eGvdKhvJAgEt92eZ3b2W7fj1Y423r7D5i+OxSTa8OZZm+IrdKbPeP3I4s6Ml5IJpK8j75oVjCYJl57uc2ZF0VwIcTRIx6aYxBQ7EXbeJok1+nSLwLPY+XPbufJ4ExVrrJxN81QLSxg8zyIKFRRd3HvG2NgI6T8wT7La40JbICslvNkcF6VP5XQdZ6GPwOC4Fq6JKQWK+skE6Uikb9Gc75OfKVIUHltXu4xWLWqjTro+zRA7UoQPLtBb7lN/skHSiWk7Lmuej6VSx+k3zoYMHm2yMRBsvaXK3n3+1SeSg9WAzoUOuYkcrTNtVr+5TtyJiHoG6du0lwKcjqK3sozcCDBSIlZ6mHYfRn2SQNE430dOVTEbAUlksAse0pV41eyRWkZGRkZGRkZGxnfJ0kYqjgJ0A3jwafiZe645e9xL2DjRwPYthg8OIaTg4JllNqI8ERpLSfadWro6nN9Pa2n2CqnYNnpqgQ89/hSBa+HFiqCzHYB8lDpCAfwIBhc28IoWXWGBSPVNs9mkZ6jdwdKbMbDRiOmd6FoeyygsrRHGEDipmDYRblCPfSLpUIz7VPUE5bxNw6+Alc4jVSqmBufrVOM+btQn9AoMTq8Bu9m4ZS/DT5xAYFi/+SZqgJmdQI8MIesNdKkIu1L3ZvC1FSydinB6IWJwZoP83hf3InglRCWH4xuiAIQAdyKtYemqECfoI4xBS4nx1DXHGD44RPt8h96VHsWZAkP7U9tg/vICQ6V0W70ri0ixm/Nfb3DuyQ4AK5cUxz6zwp0/P81ULcGvXyFGMuIPUEowbYUcCBs8SYmCjnmr3gCgOOQwNJdHRRq/4mBZkiRJxbtBkIrM/b7m9tuvber40M0OpyYtLAm7RlOxcfSnd7P4+YuoRoCseIz+bCoEj9S7tGXaTKva6aMGisQInLKD7kmkK5ClVCh0ig5JoNCRRroSYQmC9YC1o6mzOWpFLH91lbkfn3n5FbsG5xYi/q8/qzPoKypVm9/6J6OMlm282RJRLhV43fFUfrjcNnzqlMEYw2PLBkdK9o9+72JVt6NZX0tdsZYjuXwpYnbWY88en9MrioWm5sh2h7Hat5Z7u5HsqVn81tslsYKS/+23K1KGQQxljxvu4M3IyPjOyQTS15GHjwc88qUG3YsBi2sOuyJDLtGQKJQnsRxJaEAYg3AthnaWCesRMz8yzdP/+QLtc218GXBh6ygTwwVG0LiOhRUr6k9u0FkaEK8GDOU9uolgqVagc6YNZxuAwQiBjDTdvqGy1Se33GM9gE61QC6OCdYimqUCVndAuwn2+7dSm3bY+OuLCGN4/H95ilALklghHEEuSbCVRmmJEYKL/+FZlpsx0rN44nTE2ffVeMdBl8XPXGb+c1dwyw65yRxqoPDHfNrn2sT9BJMY6gsBxreJCw4jQQSehRsarJwNJi1HUNpeZLAakFg2+FCYLTDz3ukXpeRkZGRkZLxxMNf00mRkZGS8hjg2WhlUPUD4FvYr5MYabTj/FxcJN1L3XrAeMv2OSWqdHkoneCYgEDnGos2sqn5E23IRgN9L63yaRy+yvbWENAaD4Fy06XgTBlttOjalwJkq4bT6GCHRwkrNDpud6f3EYOsodXQKg2oElPcXWc652MqgAVNO16HkD8hvNNHYOAywSnuxhlySco7QcgHDcJwKhLVCn1r9MdAGLIlVnQPAmx1iSR9Kx9uaOhOdm8eJf+rNhGfqWFMl/LduSzdDiqtCLkIgvM1YPU5gqQGVPFSuLRTGBtpz43j1DtqyaMymKe+F+w+hnjwFgwg5XsG5d/c1x7BcyY77574lhfyW4Q7PDCy0Eeytpm3MdaRf9Fkdp6/jPVup1p9FqoRebQtWySVILN6yvs7d9TM4UiDe9BYAynNFBCC0wSk5+GM+YaiviqMArda1BV1IXaT7xl/cW8E9WKP64X0MHlkkd3gc7/Z0XwznwGmkwrvvCtyqR2HUx/EtlBQIKSiMpAL6hW/UeXQJ+rFgD11mmxHIF4ty4ru4HD/ycIP1k02sfky37PHkN13e+Y5hLN/ifJyOvzeXDrzSS9P5n2O5b9j/Cm6+ej3hynxEqWyxbZt7TRHRdaFzqsWglX4vJisAJS72BM8UC5iC4REjOBQZiu6rK0T6jsD/Dm5Lr7QNf3pCEySG/aOC+/fJTCR9jcjizoyXkgmkrxP14w2O/skyTzYsGq6L9m0mtKRiDNoI6MVEnkQ0YxJL0nNcwkshuYmYwVeXaT25jlIwKfoMlySFMZ98X9M/3+bKiTWcsg3CwmhDoR+SW+sSFAvMXdkgH8YoWxLbFokliPM2zpkmRgqsjsGJOqxNVKj2IpygSy6MkXmb7jMNhu7ZRlSxOfO1dcK+QhkgZ4EQeErz1guLnBmvUjjRpdiJODU3QcdxKJ7pU//7dZ7+YsKdx89ROX8BM4hZH5qi35eAwBiDGiguDRWp5/NUoghPCIROqHRCrCGb0myRqBlh+RaFyTy9K32SVoQQkNQDlh5cpry9hJDZRSUjIyPjjYb59rN8zwghpDFGf/s5MzIyfhjQylB/coOklzB88xBe1UWXCzSCYeT5c+hcDv9DMxQAjIFvnoX1Ntw8C1tGSfrJVXEUoLeQdjxvxZqaWkYJm7Jp0lgaTWcYhAiZqiain4o40mgGFNBSII1GbKaDK1fiJhowKBe8WhEr75JXHRwdo4QFMp23arpgBBqJYxTunIM7N0I8bJEMNAjw51Ih0zqynfpDF+g7HiMB1N62g6insawEX6fNmMymGXV8VBGUDckgwSk6+JXUbTlbbrMer2GA0XL6kypsSeEjBzGJRtjPCw/Fj7+Z7v/+CMQK9+3b8WcrqTj6x/8IixtgW/Dht8K28Zc9Rv5kCevwBIOFIsIWTNyTNoqS+2eQv/aOdCzPgbFvrWf6Ul4qPBV/8lbu+PSjoDT86O0gBNvePMT5rzdZuBJRrVgcel8NgKl/dieLiSEMNRPvmcMaLWGWBzRvPYgMAozjUBlJnakGKG8roQJFruaT9BJyEw6jow7r6+lxn5m5/qy38Pgaqhnh7htF9RTB46vkjkyw7c1jnPryKgrDls0mv+XpPFvuGqM5PyBXtdlyZ+ra/cx5w9f8YeK85JlGn3sU1MY8Ju6usfZYHbfiMnHPyx+LV2LlbJehy02k1qh6j5WF9HyLheQt29L9Hm2KoDuHIGcLBonBsQT7hq9979brKR7+Sge1WRZBa8POnf41Zo7Z7QxYykk8CyaC9Pt4qmFQ2pBo6ESGKx3B3pHr3sRXhYfnU3EU4Jl1w2IHpr/9qZxxA3i1484s5nzjkQmkrxPzf3eFaC1izRtGhRqBwY4VsYJyEOAoQ7cHsWsT5n1iS9Ju2bS/VKcw38TqJSAk2rMYb/WojEhWF7uoZvrkON5IQCQIRyIEzPVabFtvYQHKGGIpWa4UaBRzXBobYu5iHdu3cCKNjwBjcB2oDAJMI4QNQzevaDcjVi73CTYijNKonEO8HKCT9Hs/FEXcPr9GazTHmu1TqXcZ2rwQjj9zmcST5FZP4Haa6NgwMr/OxtDetLOmhE7O4yt7ZpHGsGupTlEpfCmoIGhf6NI826Ywkaeyo4zRhqAeYCKNERDM9+mcaKAChZ3PTu2MjIyMNxqvVi0oIcQR4F8BdwCnhRC/Y4z53KuysIyMjDcUSw8tU38iTYtuPttkz8d2Ea8M+JI1ydEdW6k6mvvPDthxF/DVZ1GffRwVauzHziM//j7sUh5/zKd1po20BWOHUyFUo0mkh6MSIunRDVJhsWe5RLYHBvSmgXC9NknXDGOhUNiEJcHNQGxLrBxpzC8kWlow7rLrsTNoIZDGsF5KxTvja5wkQRpFKH1KjmHothr16UkG7dQhuP99qaPz8i23cmptDJEozuU83j49Rb7qMhbUWffSWqs13QDAumkLhV1DGKURjgV7ptL3JyuMH4oB85xN7yovFEcBhn/zTop3z2AGCe6RqdTIcGE9FUcBEpWWNLiGQOr6Nnf9Nwc589VV/KrHgbdupuffuTvtU7DcgANbYXL4uo8/u6fgX34gFb83xVPLsXj3b+9E9ROkb101XtgHt7D1Ez+ZCrLFNGU8P5Fj9PAo9Sc3cCsu43elx8PO2/iTeeLE4PgSy7OQUnD33UWWl2M8TzI6ev33KyZSL/v67PgQf3dfDY3gLmfAEQH5mQKdLcN0ui3CIY/cvvTYPlas0lQKqQzHRoosrytqY+BvL9Nd1ZRH3e+qZFkliBjrD4ikxNcKf5AKwbNlON/k6jTAkC/49VtgviOYLMBw7trX/3ZbXRVHAZrNaztv7aJDpWRRdNJ58mOpkFq0DY9eVkQKxgoweqvg1a9E+p2RdwTPSXUCQS5LhnzNeDXizizmfGOTqUivE8kgYfhCnfKERyAkiQWRFAz3gjSlfvMaHdk2jWKe2LIgUqhLLeIwIXZt+sVcmnoTCaylPsFqkKbVsPlzb8BoDc8FX3mfS7UKle4AJ1K0fZeVQo5EGxpGMtIKUdJCaYXf7iMHffxBQqwFiWuxdq7PZ/7VCQqDCGug0MrAIEwXtrmMRAge3D9Ls+ijgHuemacYxYS2xE0UIpAknYRVhpG2ppy0MYlGWxbE0Ck62FrT8VxCy8KPFXagiPsxbDY97J7v0b3cw54sEQfpg3OhDFGQ0L3cY/34BrXDo0g7s8xnZGRk/DAghKgCR4C7SQPSOeCwMaa/+b/fBZaAXwXeDTwghPigMeavXqdVzsjI+D5hsDy4Oh13E+JuTB2LBy4JenGChaEyYvh1YHB8ifaxDdBgF2yGltuIch7Ll6hAYWyBXUhToh2gFrQQxqCkRc9L3XSR5aGFBAEhaSp9Vxcx0r56q97ddJiuFfIMBRbSGOq5HLcJxZZdLkPhEmkAbvCn0jG0n5DrNhACLNknLOZZPl4n1w4Y6/YxUrDySFpjsjM9RnNCoUOFW/YIinmGfJeprRZDly8ihab4pl3pyvzIbdDqI56ehyM74a7Nzus7JlJh87npV0DYEu+ul9SzrBYgjGGlBXkXhouvOMbYpM/Yh7a++E0p4W03veLnvmNeJqXZehnDRftKQNJPqOx2sbz0WE+9bYLJt46/yJ1q7x7mwkMtVDdm+OYKB4dSwdGyBNPT36H4uLQBXzyepr+/51YYLePfUiN8ap1kvoM9VcS/PRWVj06MYXkJloEncmUUsLYS05uu4kxXMcDFhYRbxlxuGTF8NvFIEEzLmOkJl6Cb8Of/7jK99RBsSXst4k0/fm0X6cKTDb78pQ1ynuTdPzVOcTLPoVtLDB5dQcUau2Bzyy3pMf3ZvYJjs9vLAAAgAElEQVSjy+m+OfyCU6XsCW76DvpAjYzY5HOS/kAjhWB6+toKou1bbP/gLPVjDZySzdiR9IFFv6/ZXtT0EsGYb9joGUbz337ZrwXv2iYIEkEjgDdNiVcUizNef7KY8webTCB9nXAQ1BZabBc55isFRnsDhrsDEsASgs2kc/KDiE6cEDoO5U4fL0qfxEWOjUwUYAg3EhqrMajnbeJX/yYggUSAFoJqJ8BLNH6c0NWGLY0Oa0KwMVxEtrpIpUFpqktNjAFjCSzAxJpOxcEMwPQ0vhHYymAAbT0fU6xUCjQLPph0uQsjJW67sIwvQJj0hFt1xigmXZSwWPHG0UisJGEsWmNsGc5NlunaHvkwJj+IGChDPkkDzee2reP5JH0Qvo+bhNgogkjTO9fF+uQFkk7ClndNvQZHMiMjIyPjxnH9NwVCiA8AnwQGwDeAJ4E/4uqjOz4CzAL/vTHmUeBvhBCjwD8XQpw0xpy6EWuekZHx/UPSCGn+/Twm1lTeMY07de36loPhHMd+/ySyH1F45zYOlF02jq3RsWwMBiVgcTmNvxuiBD2NCQ2R4+Pi4fcSevN98hOpo7DxTIux20eRQqCwEcJgjETmUzFNv0BE05uFHnOeQQvIxRGB4+DJNDPLdqBJKqg6JBQnyjjdBk2nSssp4+mISrcLgGspQttBGlA2VCxF2Aop9fpAWguzutECoFC1ERUPGRmsso3vCkTOYfbn30Tj0YvYtkX1AwfTlZQS7jsIe6Zhrvb8jrv/LnjifBqY37rt+g+SZ6cCa7uf/i1cI2X6+4iVR1ZZeWQNgPqxDXb+/Par7tKXpu5fXEwQt4xjA21gY0Ndv2P0zx5O9w9AJ4Bfew/Ctaj+8kF0mCC958cb31+h/VgDnRhG95SxLIHnvdgs4m12Uv+NDwyR+2yTZt/wvsM5RoZsrpxo0TvXwAzSm8f5R801BdIkSPi9v+lwIi5iC8PKX9X59V/Pc9uPTkE3YuV0h623DrP9jtTR61qCu6avb9NfiOtK7r23zOpaQqkkqVZfeT/mxnNseXfuxe85gr0r6+SafZpbR8g5Q9/9Ct1gco7gp/db337GjFeB64s7s5jzB59MIH2dEAYK0wXedGmZ27QhsQVKpgWZjTEgoG/bPDY3QS/nsLXZYWSj9VyZIbwwRtlWKlAajQ4VzxW3kKRBkJECAdihQghBkYi+Y+NHMeVBiK0NiWMxFsfYQtDN5yj0BulnEYBG6DT13VIabxAhBoLSRg+h0lpGkWdhhACZNn1yowg3ViSWQEtJKYyRgKUNyWbg0LcLhNLDUgoZSwrdmDGuUNRpjZiPfvNBjtYOYClNIiV+omgWfKr9AMdAIgWJZWF0mg3Tz3lYWuEHISZUrJxsMXnkO++MmZGRkZHx/cHLpToJIX6V9Cn8c/yhMeYPX/D6MnAW+GVjzCMv+Nxzdxs3A2eAx1/wmT8nTX96M3BKCCHMC7tFZGRkvKFZ/69niB+ZB2WILraZ+te3X7PpyVOfeIrmxoDQspn662eo/+YeRocctnV6nLU98lpzYDSNsqO+RfeJGCsKCCY8Rsselm9hW5rg8UWEb+PvSp2XbafAsGxjG0UsbQbWc3a1q7lesFmarrCzRO70OhpJIYjo7k2FqfW5Gt5yG0tr1kZGQCvOP6tQXg2JJpIeHW0zAVS0IQxdtBDkwoTCrgrlQzXGJlyaaxG2NGx9b2oeGB222b/LI4ih7KcCFED+o4dwj0wjfBt752a6+vw6/Kd/TGt0ujb86rthtIxqhYTn09Je3vYI63rFv5VWKooe2HSFXl6DO3Zd3xivMe3TLbiwAnHCoD9C3IlxKy/vBs3nnxcnpRDkrtcVaAx0g+dfd553Ogf1kN5Cn/xkjtxmCvlPRqv8VbdHjOA97T5S7GBkxObgwTxX5iPKFYtdu9J5x0ds/sVNEboT4e5Onc0VmWA1+/R1ut4jzdbzq6INhAliM/e7HRj+cZCjmwiEgM82LX6dtBHWkV/Yfn3b+R3i+fK7qtn6HHc21yg+foZeZKitLjP9njuA739RPuPV5aVxZxZzZmQC6etE7b4Jlo/WiS90UZYgkRJpDNI8/zV9akuN1XIBYcGZrTVmNtoUenEqeiYKK1FoKUCbq47Rru/gJBo3UVjGIDQ4A4V0Ja6lKA4itCWwTFonJsRhYFsgBcq1MKGEOK3pKdXmE1HSUC7fC3ADzXrR53xtiOl6i4luj1bOQxjwlKLaj7jn5CWahRyhY7F7eQNhDGhNjoRuvgDa4EUGqdLtMEBeBZBmG5FPQkZ6Azq2g5SpmKw2BVeUTsVWSyKlQCGIPJtiX22KpobuSoTMZen1GRkZGW80Xi5a3AxM//Bl/vUcC0AH+KgQYgY4CDwMfH7z/22gaoyJhBCWMUYBzwC9zXnh+UtdRkbGG4z6sQ06l3oUtxYYvSUV9dRXL8FiGwDdHIC+DSSc/2//jtbRZfyJArt+/33Yo0VObgiemNuOEoLJfp/bHl9j/OAIt166wrtX1ghdD7PzAAD9P/oahV4bYyxyVxapf/okW37tVvp/coz62QQhDUNWBD+2lU6xxHlpkVMBHaeAPZK6WGPbYmVsGAxMrKU1OEtAJCRSG7QQFDfD2B3bPM5TQQnBdN7gVnPk7tnD8hMtPDVACRs1tenyq1aQzS6W1hi3AOsBVsVn578+QudPjmPVCgx//FYAaneM0l/qE9RDhvZXKcyk4q2wJc6BF7hEAc4speIoQJTA+RUYLTP41DOo1dTcoNf6FH/jyPUduIkq5D3obza42n79TYFea/JXFhhcSh2kbqeD7R685rz79+cwGro9xdycR2Gz9ALnluHBp6Hgwftuh1Lu5QcQAt66H7584vlpYLAWcO6TF9CxRtiCHT+9jfxEjqHji/xisimongQTzyGcVBR9Thh9jvCL5wm/dgWA+NgKxX92BOk73K0bLEQ2BaGZG0uPh1rr0f9/nkJ3Qpz9Y+Tu30diS4K8C50YIwTd8vPj9xoR3XpMZcLDL37/SA3WYpt948/dHxrUchdZzQTSH3ZeGvhlMWfG98+v1g8Z1ZuGeGKsilvtMdINcZNUlFRSILUhkYKeY6MsgV+wkZYAx0KK+OrXSQByUxw1gBHgaIOyLAIDGENJJwhjsCJFIVE4WmE5BmXb6MggE4VAgCVwjUF4Ep1o3EghtGbg2UjSOp9enNAo5Pn8bbspBCE5lZC4FkP9gIXREhMbXTyjqAwiKoPo6jpiNEKAQJIPQzaKRbwwxgvTLpmJLWjKKiOqDsCqN4aVGHAFA9fGNjrdOGC96LNeztPzXaqxphyFjK83MIZUQJUCb8hl9ZkOOz7wmh7SjIyMjIzvke+yWH4TuAL8FLAb6AMfJX16/wHgPPALQggJV5Mtljc/NwaQdRjNyHhj0j7XYeEfltLps22cgk1lVxmv5jFYTO9B3VEXDKx9+iTLDy4DMDjX4+K/fYSdv/suLuSK2InBxrDu+QSezZUn1jn07EmMFkg063+j4XduQy/0aIkyypK4KsZ9Zo3m8WVWzmgwEgyc/LMV5j4BxdsmWH2igdAGbUv2vz91Sq7Uhomc1Im3Opam+eZHPUIpUSLNIisMpYUZ5352D+t/dAZizdSPzWDZkt2/sofWHz1BO8zhCM0t/zR17Dk7q6hLqetPOhr77jl0NyJ5fAl/X9quO3zoMrmf2INTctj1kR3f2U6eeUGrbylhOhWhdSu8+rZuBS/91LeyuAGDCLbV0nEKPvzKO+HkFRirpM2Svs+ZGg3xthiSBIZHY2QYQ+7lXY22LTh0y0uKXAYRfPLhtMkTgDbwc2+59gLffgBu2ZbWIC2nY3Uv99BxeskyiaFzqUt+IocczV09DrLqwwt6MRhjXuSgVvPtq9O6HaJbAW4tR7htDO9kAwou3JIWC40enkd30mMdP7OGe3GSoa1VbtrhcaHhIgXcsz1dVns15Pjfr6K1wXYlt/7oBLnSteUGYwy6lyDz9tVSBd3I8IVLhlDB27YIJovXHxc8smj4yhVD2YX79whGcgJ71zDxkysYY5B5B2u6dN3jZvzg8V3EnVnM+QNOJpC+DnQudbnwB8cpPblBqRuRaMMTM+MMXBtLGA7UGyQJ7FhvUC/n0VHCjmaH0UEfJSAxL+66ZwAtQGrIDRIiS9DwHTxtUEaTeGl3eozGNzFWAgMhMYCXKJxmF1F2GNlTYn1FYHU7eP2Yr+2fYftaA1frVCRVsDZWIrIkFaVJbItuziMfxawV8lTbAzYbE6YJ+iL90XEgTcMXAtto/DhGuxZBwVDoRvja0HaG6doFtCUZyByWUvQ8h4Hn0PE9/CiiFIR0ci75KOLyaBnTDdi12MJRCaFlb/ZwEthWVtg6IyMj44eIAPgT4N8CjwFF0sL5nxJC/AZwFBgFdhhjzgAYYzpCiBKwKoRwjDHx67PqGRkZ3wthI0THmqSfYOdtwkb6gL744zuwfJlmLb1pCmFL4m7yos/Gg7RkXKkf0y9uNtBRBvv8Bs7xpf+fvTcPs+u86zw/73v2c/faV1WVdlvyfm3HcWwndkhIAiEQIEl3CKGhWbr7gR6mG7phhuGBngeGXqaHaaAXYAgNTQgQCIQE0sGEJGSxr+NNsS3JWksq1V53v2d/549TtixbkrVXyTqf56lH5x6d5XeXc+/vfN/f+/3RjQw83ULGCYWj6SB+Xdl4JQMEaJFFfrYLT9eRiUpnTAlBFKeVggMTFvPPG0RSYiYJlaG0Wi3SJFqS3h+Herqtff8Ynb88idkJiByd/gcn0vM1EsbePQ1AAERhgnp6jt1TIarrIUyJdSKNLT+VIMch9gXuWIxsdIjRUNHpe/FXipoXzI4x+AcPplPgt4/CeCqYWvdP4D16FADz/snzHAD46n746yfT5Z1jqSgoBPQV4P6bLj6mDULcPsPAqbX0wfQQlC6yy48fnhZH4cwp9OeifKZ/rjvinK4/E7zsfeu8/yb8LxyDIMZ8YPJl27b9X15l6UiXXMVgzyODWK6GvnuA+Rca+DGMzOSQFYegHnDIcGkNCISlkesKZgBsHaIEFcQIx0BYOrom+OWHdT6xL6ZgCb7v9lRSWJntkax3m4+ChPopD6dw9uZbiR+z9LsHCE520Adshj66Cy1v8GcvKl5cS48x24SfrIImL/zeru4pPndUoZSiG8LnjsKHbhIkW/vZt3sb3sk2g/cMckvhArpDZWS8liznfIOTCaQbQOOLx2h+dZaSpxFKwcGBCnOlPFIXYGosGQlG3UP3I246Ps/WXptdnXliBCoGTxkEuRJ+LJA5jUQIEj/B6kYkSYwZQaJJlGugqYjA1RGWz3RnARlAU88RWhWS9ZFuTVPEeR2916NkCxY1jeOTA/R1ephxQiIUiSZx+izycUQpDlktOpR6HsVuj7mBIs9tGeK2E4skukBTCrU+Ld5UCqEEThQS6xLfMHGkItR1fCmoeF2syEcpRSwFPaVRiXvEUlCOAiLHxK+4SF/HWVtj2GuCgruP95gtVDDCED1JsIlpGA6JLhGJojic/ehlZGRkXH9c/ADXuo/Tn79iVRP4rBDiUdKk9feBBdLR/l95xXa7gc9liWpGxvWLM+yw9kKDqB2i53S2fShtFmQ/PJN6aIYx2ta0SnPoe2/i+f/yAp0TPkZOcvOP3gXAnXMLPDU6TE/X2L1Uh+Yo3sE6pwp9xJoGCvJ+2ijHNyU6qQirDIl/cJXgWAMpo7QyAIGmpV8pss+m3PNIYpCmQFuvCh2rt1goFRDAcLOVHmugQHcwRztvI0yJGk6r23JFjW4nFXJtR6LpgmSyjNR1VCGt2pNjaXmCsE1sNwJLIQ0THBM5lMPY1U+4fwWhS8z7Ji7thd459poKT+vBKfQ9g+m9xOu1A3/y8OnlA3PQ8SB/jqnlm5l7d6YVtB0fto2c7lJ7oZRycOdW+MZh0DV44OLF4dy4y8x3TdE+3iE/maMwlQqQ0jVw3r39jG3XTnosHk5tENqrASf2Ndl2T4WvjY/zxG0mRjdAu2mQH5aChS6s6AZsSaubD7YE9wDGzn66H3smrTK9axQ5nAq2OwZ1fvZtZ0oJ+f7T1bRCCPJ95/YM7e5bJTiZxhYte7SfWKL00Bhrr9CMO6EiiAXnc05TShFHCbqx3gRNpeteIl5f3H/AY95wYNqhvggjiyGDQ8bZDplxQ3Fx13CWc77xyQTSDcAmIIkFLgktQ8MSMeXQp51zEY5OuQctBcQhQ92ALZ0m08kRZu1JIqWxVS5i7O0nmh6m7xYT7Yn9zH2pw8mmTagkMlQIaRAWJFbeQvN8SssdClqItCRO3CZKTJa1IsrUiCaKjB6bRR4PiWyHI0OjqEQQaZKy52NIMF2NzswASTvmzcuLNDFYmygwN+RwuL/EYNFg6339xAtdmofbqFihK4XVbyOjmKKI0SWcXFMkQYxhKOx+g1KoyM83UcQkukBPAlbMIUKlYyYKO0kYW20znHjsCVosuA4Luo3TDqjUF4kROCLGAuI4Iq5YjNxeRsSZrUdGRkbG9caV+OZ+heeTD5SUUqtCiP8G/DMhhA/8FvATpAVZX7oCp8zIyNggegs9yjuL+PUAq2zSm/cobEkFI31L6Yxt5/9+kbVZINAJQsGB/36Ue35lkL5ehw8//xX0JGLBGiRXGuPESkzPsJBKoQSsuekxnahLaJiAQKgYu9+iMmPTY4mWUUGohFK0mG67tYR+c4WkG6IVTYyRVESc0gL6Z9Op/oXBVDQNGwFRzkYZMUKXBI30HvrWe4sc3NchjhRbd7sIIdD2juH8iwcJP7cfOdOH+SNvBiC5aTvqyyfAD4knxxHlPEIInA/uwVrsIHImMn9asGocbOKt+pS2F7H7L62wQOu/wArKwRIsrjf9ydvnnJZ+XTAxcHn7v/ceeMtNYJupB+slUJjKvyyMng/xqsrLlx6/uJbQnFhvwhVD3YPKsIW/Zwhz/zKJa5C/J/WiDZ9eQN992mYhPtZA31ohThSHVhJsXbClkiqYg9MuMEBz0advwqEwcO73WTpnyhDSTh+/aVTwmSOpyHn7kMAxzi1gHT3S4zc+Vaflw4O7DD74vgH6HMFDk4IvnYCCCd8yle7/6knNSXarmMHl551ZzvnGIxNIN4D+d29j+kuHCJ+MyK902b5a51RnhZPLOZZKRfbMHWZ8+ShGGJMIkyDXzwkxRSh1sAVNVWSybGC5Aat/cYTmgsfgiVOIdo6Om6drGIRb+ok8hfRjSms9KsSU6RH4CUro5PL9uFaXZt5ibiZH+akGhh+wv1hmLS8ZbnrYsULqEa5uUJ8ZxLcttOYaRhzy4LFjhAtdVgdy9Nk7iQsDTG03kA9u4chfzNGd6xJ7MWbTpzjfJG/FLJoWkW4iowQZRoyvLdDXriPimEBq+E6MFgvc8Rxyts1aEBN6EQU/Ihf4uLpiZrXOSCiZM/PUTRsjjrHjiKnOAvO5Mou41F9oMP7I5vcxysjIyMg4k0v0IAVACGEqpQKlVCyEeAvwLuDX1//7PwIh8IPA/0k64v/TSqmvXWbIGRkZV5jYizn26Vl6ix6lnUXGHxk9Zwd6aWusPVcn9mK6tsaWd5+7QvLoHx4m125QiFt4wmbhr034lbvZ3jqCk3iAoBS0CA/tROyt4J9cQ0sUSgi0JBUsdUvH9DuEUscNe5iP3INxyziVaI0Br4kgYc1JKyOnHhlh8bkmwWKX3EyB0btSkWniQ9tY/dwsAAPvnQLA7Tdwu21Ep4dybHIDaWWbaUn23PVar0TjPXsw3rPnjHXKtQneciuxH2ENONALoWCnourwmWLa6r41TnxuDoDl2go7P7INo3AVq+m+vQpFJ/UgvX83aNrr77MZSRJ4/MW0gvSurWlF6KXQd+X9L2ebik8eVIQJvGtGsGdAUBmzGd2Z59RzTYrDNpN70/NOlwSzjVQaKtuCkgWGJnjP+4b4+qlByja8c2a9WW/+9OdCCIHIGSil+IOnQl5cSVXHt2/XectMKisMTrvrQun5cW4qU3jzCN7BBuZkjnw1FZ7vHhVsLYMfC8Zex3/0T/+mwdJCgIoVf/NMzFuqPhMTFm/bInnr5Jm+qzt32SwvR7RaMZOTJkNDGyODLHcV+5YV/Y7glsHMFm6judS8M8s537hkAukGICp5xh+ewD34FI2liF47Ju4FTPurlHvz5JM6pvIIhUNQLhD0lvHsCrLnI4RCJRLz848RFsqsGRVaHUlgVxikTdFuMmvk8RZ9TEen5AXYzR5Pjg9w1JfsaCxAongqD/N9ebaoNve39hNpki4WA/UOIwWDkVYbDejvdvAGKrQ6EaVTi2hBgECxY/kZghWH+imLnfuPUDE03Jkc7BrBNIfoehFaIyS32sYIYzxfIbQYrRRhhAl2HBEHECcauoixhIen6SwMTTF2ywgdfRXVEBhxTKnRpr/dRKOHUqCkgUFI2VBYoU9fZ4litExcVhBbrIkp3OGsK2FGRkbG9cZlJKoTwE8JIY4Ae4E7gI8DvwSglFoFflEI8UdARyk1e2UizsjIuNIsPbFC+3g69Xb1mTWKWwsUt55dUEr8BHfMJagHmCWTOEgFG2/Z48THXiAJEsY/tIPcdAG3vsJA7xgikSBiVurpMfJ+gCbSZql6EpOstZFDQ0i1kjYARZHo6XdT1y5BaKCrkGWzwGSYEJ3q0BSDKDvdJonTajpTFwz06/SkRbFPR1v3yN/xM7ez9vAYaIK+9Sq98uoSw8kqkQEaPfqXTwFnTpd+PerlfnoH9yPihETPM152OZcM2TnRfXk59mN6i97VFUhtE955x9U7/rXir56Exw6my88chX/27nSq/CbgLw4p1rxU9PzTg3BTPxApxFOnGNi3jBh0ie4tY9gaD89I+hxBK4DbRwTG+mfz5gHBzQNn/g5bb51GeTHJUgfjzlG04TydQLH/ZEh33kPogqds92WB9EIRQlB+xwS847WDGv3Oa3MBpRRBI0R3dTQzvcb8hR5hO7WgSLwYFcZnHP+VOI7kkbcXX9Ow6lrSDhS/9ayiF6UtlruR5N7RTCTdSC4l78xyzjc2mUC6EXz5eaJPPIE8uIjXtfBjG6kizJ6PHXXQiTCTgFCTiJZExiaDSYu60ZdWkSYK1WkRNzwO3TpNodWDKMdJy6G/t4qvNNzEQ/USinGLJ6YGeXZiCE0N8Iwc4cEDT3OiXEIpjSNWhenWEluHbJpzPjPNBjv2LWIpH6XHWIR8ZmqEbpjQ53kIBcOrDTqBzapdQUQKww/p+AqhPMp7faa2hIinQlSrh9EJcESbSGhoWokx/xSDvRUkkihxseMumkg45ZQ5PDBO6a5JKj+8g/HpPE/986cpPHOKitdFuhKjFRKFkpwj0HsNmtLFCTtMRCsIpRAtD+F6NGybaIN++DIyMjIyNoRVYCtwO3CANEl9VCnVeOVGSqkXNiC2jAyq1epO0sYO/cAK8JFarXZwY6PanKhX2SS9+vErMfI6uqORhDq6q2Hk01ubZ//VVzn6pWVQsPi1Be7/s28lF/WIEwuhBApwvTYADa2PSrKIAnoUcO+cxDjQxSDG09IGo+b6fNx67GDrCqlsAqHTXEnI25JWrkCo6whARmkTnrV9dUSicEsmUTuieahF354yQgj67hs+43m4us+2coN2aJLTQwpmcNGvW8PX6T50GyKKSRyLwUaIM3h28S4/nWftuVQh1hwNJyssuDBml08v1ztpk6XyJVaRXmFeeZkoQCloPb2M9z/2EbcCpCFZGrbZ8mO3IITgjgsU5oSp4bx35xnrLKkIX1ij20tPqskAHhi5Uk/lNSSx4uifHqN9vIPu6sy8fwpn0OaRiYTlpZh2IrkrFzCQO49Z6TobJY4CLHZZF0dTjjVUJpBen2Q55xuYTCDdAJKFBodWdMK4zHLOQu/FREpH1xPGggBbeWgkaDKiaysK3gqGr9ACOJUbxlAhR41+BsM608eOgm8QJQ4qEAShxBA9SoEPImaodwJvpIKuImw6hCR0hU6CAQriAFqnPLxWE88ooYXgBGAnCj0J2D88yHOlfupmDhWafPjJL1Ps+Jxwx1EqtTX2NYf+ZBFnqYX/hydp5qdJ1BhGs8twchJLeSS6xBqIsXpNICEXd9CDBj2jSKBJND1Ej0O8o8s88z8FjxSXeU95hcd35UkETK8tI+MePeEgwwhTQEVGuN1VRBITAbm2hxMfp5KP0RaHSO9BMjIyMjKuFy7VC0op1QW+7UrGkpFxhfnPwK/VarXfq1arHwb+C/DwBse0KRms9rP2QoP28TYDd/RR3Hbu6chGwSBsRQSN4OXHAIceXcJshwilmH1SEXsxswcShmONWGpoSUzdS4WtZ0bG6BZ3oJEQBor37+gnOhHScE1EAr6QtPRUeOloBp6WR1MJodAoh4r2is9cfz/97TaJFCxW0qZQRvHMikzzPBWa8pE95D//LPmFBlQKiHfdetGvW27UoTPbQRk6Rl7HLJ77fJXdJXRbw1/1KWwtYOSzZjUXxK5xeKmL/WgltQ3YJLxnq+CPD/DyFHtNCoJjDcKVtOtREiR4z60AaTVm99lV4lZI7tY+tMLFecIqP+HdosXThomN4k2Jf0Wfy6G6wotgVx/oUtCZ7bxcVR51I5afXGHyHeNsv2+Ah2fn8MOErTsc7IHN3aR3JAcFU9AK0mxnRyUTRzeaS8k7s5zzjU0mkG4AKyMTLJdPQgGSlmTNstFVQM4TJMogwSQSFj3Dol4sYMURvagAsZZOC0JgqBiFjqkCPKEh9Qhf6pR6XZwkoWeYFGkyHK5wz9zzzFfyiILJaJ/F3q+EHHc7HB0oMdFc5tbDhzDihAHNI06KCKFACbo4rNhF8o0Ar6hTCloYYUjPMCnEXVa0PhIEJAJT9HD8HqFS6FETx7MxohBHtZEiBkswbcwhWj5xGEGSoClYyPdhRDDQ7mIGC/h06P/NZ/DzXQbbTd7d5+IvB0SNEIXA1kk6FCwAACAASURBVAWr9ghGHJOjS8eyWNAqOEGEYTXJS6h0WpSf2Afv2vm670VGRkZGxmbi+rtZEEIYWVfSjPNRrVaHgDuBb1lf9QfAf6pWq4O1Wm1p4yLbnPhrAbEXYfWZ+KsBUS/GyK3fspxYBk3CaNpgpnOyS27cJTeeeh5253rkJ3JUVluUex0E0PZtgnZA0IhYtgfwLRMjjEji9en4RZPYMIiEQNdCDn99AWVDz4Ziz0MJWCykIq0jPIa9OWKho4RCFCcYuXuU5dIxlopFlBDkZPp10H9bhagT0T3Vpbi9SH7LeSoNB0vIX/4QHFuCiX4YLl/06zZ8/1AqGLcjKnvKaNb5p34XpvMUpl+/0U/GK3jr3lQY7XiwZwvI169YvFZsLQt+6h5xxhRyc2sZc9AhbvpIU+LcOghA82/naH7xFACd2hLD/2QP0rjw56K7GhNTDuXZVLQc2j14xZ7Ho8cTvjirXn5OH9kj0OwzP8v6eoOn2Z5Oy7aI44ijmsvuUGFZmzePcA3BP74VXlgV9Duwrbx5Y71xuL7egyznvPpkAukGoM0Mwjtuo9l7EefgCjfNH0GPQ7QIkIJIWRjSo1yQBAMlvHbCojaKqUISKVEiJjQgDAOWCjlKzYTySgvCGDfuUBQhrmqhmwlST7hteY7yU4/R7isyVbGIpMN7XjyOOqJhenN4QhIryNHhhN1PHCokAQW/i9bQmVItRlc7jGh1cGMsL6Kv10MoDXRJLvJwtYhEgKcZVPxlvKSIEhoJEkOF5HpttFaRILZQUQxKJyJiqLlKV5SJYgM7CRk4fgIz7iHqPWKRIGUPWW+gKw2lIIodNBTJUBml1XlhyCVEo3DKZLxTx0hCbLeImctGwjMyMjKuNy6nSdPVRAjxi0qp//0s603gj4H3XvuoMq4jJoGTtVotBqjVanG1Wp1bX3+GQFqtVn8Y+GGAkZGrN2V1s5C0A7wnF5E5A+uOIYQQ1A80UJFCIAhbIe3ZDpXdJfh0DWovpju+7RZ4aA/5yRxCE6hYITTxsghZCHr4WloV5wQBmiHRlGJuZIBYk6CgfzmtBBSaJDLTmVVKk5TK4C60+bqu8HMFQk0w0zgJQClo0jVTUVGqGG2pSWFLgd0P93H4yyvoBtz7Y7vT4wrByP1DF/5iDBTTv0tECEH/bX2XvH/GBbJrfKMjOC+vnEJevKWfle+/De/pRbThHIPvT31tvSOtl7eJ1nziuo8cvPBqWCEE09+5hdbhNtKSFKaunNC+7xUuBofril6ocEccxh8ZZXVfHXvAYujeVJBd+doCyfEGAujVPLoPl7GGr30VadiJ6M51sQdtrPL5q3GLluCe0WsUWMbrshnzzizn3FgygXQDqIxYDPS6dJEMyCZ2HCJQGMpDigTP7cM0V4i3jFI60aIdC7REJ9B1CnGb3JBC8zusyRwjaz5aItCEoBivYYgQIRSRclA5A9loI7WY8ahNMBejyTJJxSbpQhwLep08gi6J0IiVzoC/xlIujyZMImVwy+I8lZ5HYAsG3BZS0ykkbWTBZaAQI+tNrDJ0O5JT9jBxAp50GfZ6+LZBszOIq9rEukNUd8nFPRLl4mNg08CMegS4WHiEykVPQgwRIMIIVELUFcRKR6x/fSkBMRbK0xBRl6HQpyUdhpsxStnoykcUHPiOuzf6bc7IyMjIuEgudYr9NeAHhRBLSqlffWmFEMIAPgmcu212RsZFUqvV/ivwXwG+7/u+bxNfEpePihIav72PeDWdAhwv98i9Yxq732J1OaDbTij3G9h9Vto9/IlDJO0AhEA+fhAe2pMKJ28fZfWZNSp7K7gjqcjT0w1UkladhZokjiJajk2o6ShACGgX0qpTJ4qJophECIwwoLBzhOOzIduXVten4yd4TurR6eVcjGYXAcRCw93RBwJG3jJOVCogNUHfrkykzNgcSF2y9Qd2Era2orsact0qwt5WJJhNPXj1Phu9cvGiotQlpZ0XLugrlX6dvZ4H6FgOVnvpcp8jsNfViv7b+l4zADAadphfXy6qgFzoA9dWIA1bIQf/x2GiToQ0JFu/ewp31L2mMWRcOpv0RzbLOTeQ1xVIq9WqDrwD+HqtVlu5+iFdHtVq9d8B7wemgVtqtdq+s2yjAb8KfCvpdfHLtVrtN69VjOE3TjF8aA73yAnihR4mAa7dxUoahE4JVbFh107scQe6AV6sExoOuhcQuhXse/tRf3uA4U5AEMZ0cXBdHxkoiAEUIoxI3AqRWUBrtUmcHPpqACsd3C0V/MAnyTms1gex/BY6IBAcGS6goegaLlFB56aTK4y1WiSeZLi+iG0ERCKHTwG73cWyO1DIc7R/jLAjcNoBpwZGGOw1Ke+o0DgZ04mHCOYW0fwQlwQpEgpqBUGMRkjCKgoDUzVx6KGjQDeILYto+yTRUyeRcYiIY1rWEMo0kUGICmK26Iqm10CLJTqSSLPRJ4Zg+iJG7DMyMjIyNgmbbyR/nXcBfyuEWFFK/f76KP6fkiaqmY/kBnCd5aezwHi1WtXWq0c1YGx9/Q1L0vRfFkcBwqNNAOYDjX3PtxEtH8YK7ETgSElv/xq9rx0DAbn33IwFdOd7vPAfnyY8XmdxS5nbf/Fe3BGHVdfFiNPBdV/XEI0umi0JE4FMnaQI179upK/QRQQoZKwQR5cY2N3PMaNAIWwTSZ1eIfW196wCkabQVISnOYzoBmEvpjXvURpMK8cWX+wwfuvFT5HPyLgaCCFe40dbeusYxoBN3Apxb+lD6FfXKiD85hK9T+0HwHnfLoyb0wrQtTmPtZM9ikMWA1OpqPgd2wWDrsCLFG8aE+cVVLfe2487f4heIhkeszCGr33DrNaxNlEnbcyWhAmNg81MIL2u2JR5Z5ZzbiCv+21Yq9UiUrX63A7pm4s/Ax4Ejp1nm38IbAd2APcBP1+tVqevfmgpSkHv8QXUSg8pJaFRwhp3iSv9+KUxRN7G+Pn3IkeKhFYejDKFtQ5Wx8dVCeqxE8hWgOz5WJGHpSUkOYfYMtDwMOiiaT40OzBcJrSKxF0gjFENHxZaFPZYDDxUQHcFAoMEA4OEplHkickZOpaNn7OwCgLLjqmUPazEg1ihGSAtCJ0coVWCSGFqgmhmEFHKgeuCJ1H1GCk1OtIlyucwSxJ9VEcaEZoIURJaMo+SgqbtErgCw/DRRBepeciilnYDHS3RGxmjXp4mdPrQhhzKb8qjORqmqRiM6uTjDgYhph2RK/iph1NGRkZGxnWFOsvfZkAp9TTwHcCvCyHeT5oXjQMPK6U2uzj3huR6yk9rtdoi8BTwofVVHwKevNH9R2XJQhs8LSQY21NR8eAnDiD9DsKMEMtrHPzsCVSieP6A5DlrhOfMUQ48lVqwnfjEftqPHqJ7YJXOo4c4/vvPrx9N0LUs2paFEgKKLmMPDGJ3PEQUY/QCiutaStHrYrV9zG5EvtnBGckxtKeCcHIsuCPUnQGmdpYAMCs2XaNA06wQajbGcA7dlJjOaY9Et5LZPGVsfty9fRTuG0a7xAZd9f0NXvitgxz8vUN4y955t+39xQFUEKOCGO/TBwFoLvns+/wSJ55r8dwXllmZTctGDU3w0KTgnTOS0uv4iZp3jzH2Q7cw88HtlH7odoRxft/dsxF4MYeebXHs+Q5xfPFZhz1gn6Gx2QP2RR8jY+PIcs6MV3OhU+yfJhUUj169UK4MtVrtywDVavV8m30A+G+1Wi0BlqrV6p8B3wP826sfIZh3jCA0kU4XMjWUWyZ6aDv2gRcxwwhuGkd+/kk0G8wdFfLdDr7S6NUFXgdUS1EyJVooUInCGTbp2nkkAfqpU0gSyFmEmoNx6wRqzINvHEf6HsJQSELkQB5rxGR4G3SejRFhhC4Ve7srPD21k+PjLvcHixTH8gTHA3SvhbB0cAzwY3QVIaIIUTBQhmDbqMnB7YMs3beD6RdWyMkcImeQtxWN4T7CvnHkwRdIljTMneP4z80SdBOamkW3v4LR7eGFDrmuwi0GmMMOqlJEThSIbUWwLAhv3QFKYS7MYXtdktF0GlUyVCS/Mo+kgyg6YGvgbu4uhhkZGRkZr2UzekG9hFLqS0KIf0A6iv9N0kR1dYPDutG5bvJT4EeBj1Wr1Z8D1oCPbHA8G47QJHz3bl7822Xsgs7eB4cBGNA8TpHerEoBA7oHScKTlS14lfTWpayF3AK0n13gRbNCT9Ox44jKc4sAaEoRrJ9HKgijiOJdY7h/cQLaHkgovjk1AjQqNu6KhwIMmcBAGWvQpbK7jDjeRbMkgw+lfrB7/+VtPPG/fYPYj3EnXLZ9cBtSl+x51whz+5rolmTi9qx6NOONTRwkzP7VSdS6oHjyb06x7QMz59xeaPK0+KSl9VntleDlafcAreWA/skL90F9CX36wq+3uqdY82C8AKaWNrR64tE12vV0wKW1FrL3zRd3/bojDtPv3ULzUAtnxKFyc3b9X09s1rwzyzk3jgsVSH8e+PfVavX/AJ4AOq/8z1qtdr29WVs4s8L0OKlR/mu4Kmb5hiT3rhni402UENi39GPFLUQSIzTgxVnE9luhGyB1gTFg0Il0gtUQYkEvV6AwZmFaPlErxL2jQuyMwN+36RaH0YMeraCMN7oFeUgw6C3heMsQ90iKAzDZh9gzCT9wN5b+JKoyR/T0PBoRu6oa/6a6QvjeKm68iyRMaP4/TxAdboLZh7PTIf7KEfwoh/RCtPEcYs8k1vffx603jyBsg87vPUvnNxsAaGWLmY9sZa2hSFp1uHcbsa2hJidQx1cJTyWcdIZxvS7FUshytMDQUJsop9NeTlDKw5noQ+sXRLelP7zGN+ro/SbafRPE822S8QFkOEP85RdR7YC4Moo1UNykX3cZGRkZGedis4zeAwgh/vwc/7VMmgf9zktT/5RSmWH+xvDzXCf5aa1WewG4d6Pj2ExEkeIrNQ/fdMGH+Okud92V4+6PbKfxL75KvasxOaTY/h3bUFIit/Xj7lsCIdDuS3PyJ2SBlhaiJ4q2ZvIERapAomkYKgZASRC+Ysxu0FQL1EUeN/HYbqeVc85dE4SPn0LECaqSI7clTyIEq9IkGNIQQtDspdf64F39DN47SG/ZY/SBYYxceivlVky2PzBw7V/EjIwNQCUKlZz+xY6D5LzbR9+2i0c/uwDAI9+WXrvlURvdkERhgpSCvomrW3l5rKH4vecVYZgwmJf84C2gJeplcRSgvnxpzcGL2woUt236yQwZZ2Gz5J1Zzrl5uFCB9C/X//0kZ36OxPrji6pnr1arDnA/cLBWq51vKvyGczXM8qPnlxGA+44pkqUuziNT6HIJRIRaaqNKLo2bdrP4n57AmO2iRxBhERUkYcmhckc/xnCCWfAxF9tEto3b6NA1bayJHFEvR9gqYOQE+sI8veU6ShgoXSG1GOct08i7JhFDZfSbh9GeX0M+PIPRXMW8zUU0Wxiffgzefx/yd79AeTxC3dqP+KEPwHwdWX4UreETP7MIgwXY0o/cOQSWTvzFF7F0H94+QdQF465RnPdspexFtF44QXSsQQR4TeiUJyHssWV2DdXpYRga4cQI/mDAKekikjr2yRV6bZPij72Jvm3rP6jvehv6Zx8HpdCrW+CHHiH8k6fw5wXx6BD4FvKJU5j3bO4ukxkZGRkZr2ZTDW2daxrTX1/TKDLOxxXNTzOuLb6f4PunhZVmMxU01wKb/ge2UV7qoE+W6LQV+bzglnGbpWMmQgjGt6RT8xsdxYrrYIURvqFBNz2eZ+mU6j5CKRolG6PiQuyz2l9k2S7iRj67wnRa8N2/VOUbv/AUQT1g+z/chjvsMv9sHb1kEhGiGRJ/XTc59IdHCBoBmiFZ/Poyq99co/+WrClTxo2FbmuM3D/M/N8vIA3J6Hr197n4o6DM3L2pTUXdF/wg4JYM7vj2EZoLPvl+g1zl/N3fL5cnjwS4nzmG1vLpTZY4umWC3f2SypDJ2mJabz44ns1AvPHYNHlnlnNuEi5UIH3b5ZykWq3+DvBYrVb79Wq1agKPAXuAoFqtfmetVvvs5Rz/EjgOTAGPrz9+dUXpVUWFafJmzJRhpoxxxwhiyzSEXyPOuzRLk5z42FE45qM6Gr0hF93V0G/uQ/QU+mSB8r++DfHZZ4m7J0ienEfTJPbeYWJtCG1bGfNTh3CXjkIYo3fr0OciTBuv3E/uh+5H25aazatOiL47XebLiyRegpaX0PHhqSMQpqbTYqUFR5fgpgn41juRjx1E7BxF3bsbsXsEkbNIHjtK8uh+4qUush3g/tMHMO7ZQvD4HPFci3ilR7zQRhgawnGROR2OeOgyQhUNnEHQbh7A3NuHeLZOHRexkke6edT+iNG3uTjb0x9XJspQ78DWYbBM1I4J4gO917zGGRkZGRnXD5tlJB9AKfUDGx1DxutyWflpxsbiOJLBQYOlpVR93LIlFUgaL9RZ/MuTJF6EXm7RedsIuSGbykqT8Pg8QpMUl9Pu2Xc06nzGtehoFiKJuKueFg17BZs100EAFjG6rXNAH2JxYAXND+jYeZ4dHOYRIDfs8MCv3XdGbIUxh7CXEMWCKFaYhfSWSb3aozBLNzNuUIbuGWDgzj6EFAh5fpFpsXt6eeEVdf5OQccpXKgccXnkDqygtXwArNkm9kIH+gvc8dY+5o/10A3J0GQmkN5obJa8M8s5Nw8X9I1Uq9X+7jLP807SrvEA7yU11B8B/hHp9KhrLZD+EfCPq9XqJ4F+4H3AA9fq5MaeQcKnF4gOr6EN5zHvGYOciffmKt3/eZTW803Cx+fRGx1UGCJFjNg5iHFLP3qfy8RHtmH020ReBFr6gyTiBG0sj3nflrQ7oN8i/mIDrWyhjoDXFPihhZcbofj0Avq6QGrsHSQ6WgdAq04hzQYKQWCUiP76GObCEvrugbS7YWXdzf6+XXDfLgRnjrmolQ7xYpfoubTnQPi7TxM3I/wvp01a4wMrqf9qwUSPFGalgOe1sSMfu1+SBGC+exTjjkmS+rP05nqERZvczgpGXic43j4tkI71pX8vvaa3DRM+u0h0vIE2VsC88wrZIWRkZGRkXDM2qxfUSwghqsA24NNKqY4QIgf4Sqlog0O7IbkC+WnGBiKl4P778ywuRliWoLLuL9r92jzhQockgaTlEzy3hritn5Mf/yZBAAiY/dgzTP383fTmWuxN2kRSoicJPZEqlnODZazlVJVpuDliAfZNQ8xOtFhVGjkSpvZWzhlbHMPomwdoH21j5HWK602atn7PNJ0TXcJWSGVPmcqezG8w48ZEKcWprsDSYeB1mrbfNgRPzK8vD1792M7GzUOCXknQCWHYhYFC6oWq6YLxbVnX+RuVzZx3ZjnnxnDBQzbVanUY+KfAzaRi+zeB36jVagsXsHsFWFxf/lbgT2q12mK1Wv048LMXF/LrxvmrwHeRCrCfr1arK7VabU+1Wv0M8HO1Wq0G/HdSH6iD67v9Qq1WO3Il4zgfQpfkPnIrKogR5unZX+HBNQCsQQu33aRt2Ggk5FWPvo/uIrx1kvyki1VJR7fkfTOouQbaTJk4kug7B7AemQbAfddO0NOKyvjOERr7TaJ2TGl7nuiZBZK3TiP7XMzqGHI4h2oG6NsriDAk/OYS3mePQP8Aqu2DmcP47jth5NyJJIC8fQL1iWcAUI6JKuUIXzhdLS6HcyTdEK1gIqIYs9XG3F3EWAtxRxXMDMP79oKuUf6Xb8I92mT5U7OoKAEpsF8SR8/2mpoauX90+2te04yMjIyM64fNMpL/aoQQw8CngHtIw9wBHAb+A+ABP7Fx0d3YXGZ+mnEVUEoRtiJ0R0Ma8rzbSikYHtZ5yVsNoLfYIQliYiXQYkVn2SNo+3QjCUhQ0OmkQmjXV4SWTseyyPk+PS9df7KcZ7AVIRPF7FAJIoW+rczCHZP4x1r0yhbxA6PnjMsp6eSHHcxiWtVaHkn9EYszBe75pbuI2iHWgI3UNu/NdUbG1eST+xXPLiYIBO/ZLqiOnfta/7atgpv7BQLYWr60a6ZxoMna83Xsfouh+4Yu+tobftMQ+cM+0bxH/11l8pO5S4oj443FZsw7s5xzY7kggbRard4P/BWwAHx1ffWHgZ+sVqvvrNVqXz3nzinzwN5qtXqKtJr0h9fX54FLc0M+B7Va7ceBHz/L+ne/YjkGfuxKnvdSeLWQp08VCY82MPstRqdDYhWi25Abc9DfvwucM8v+5Z5RxEQZvRvASPGM5JL7b4KcDatttFunqDx6/OXKTqFLhH36rdcnXyE8WjrKXPeAEYJ4eoLkoel0av3rPZ/REvpPvo3w/3sS5VqIvIX10BTep/ajogSjOor9LVuReZPux7+JCmKQgmhoCH7mzAJeWbCwbxlkuGjiffwJLDPC9JqkH5kLf00zMjIyMq4nNq3Y8H+T5kD9pDY9L/FHwP+7IRFlXIn8NOMKoxLFsU/P0nyxheZobP2uKZzhc3emXnx8mYWvLKK7OlPfPok74rAY6SQJgCJMYEXo7DB1UBpq/XZWqDTfW9Lz7BsZRGiCJFbsPZHWY0w0mrhJB6EUck2Apmid7FDu1qEvAUK6R9Zg+9nL2QxL47Z3DbN4uIOT1xnadlpMMXL6y82ZMjJuRDpBKo4CKBRfn4Pq2Lm3F0Kw7TKKrb1lj+OfOYFKFM1DLYQuGX7TxZWifmNN8sVdk7ALnpOCyZ6iz9m0OUfGNWNTfgaynHMDudBf938H/AHwo7VaLQGoVqsS+M/Avwfe/Dr7/zbwh8AcEAN/s77+XuCFi4z5jYVSsC5s2m/dgizbJE0f40PbEb/zRfBD5Pff/xpx9CVEyYHSORLP22deXrTevQMUqLaP+ZYphHtuI2zjlqF0uvrhtXS6+t3n+cV7FdpUBa06gWoFOO/bhTacRxvLkyx10SaLyFx6Xn26THggrS7Vps9dGWo88TxGbxF6wCf+Hv75t0H+3Il2RkZGRsb1i3e0tdEhnItHgEeUUmtnDEbCIVIf84yN4XLz04wrTPdUj+aL6XUc92KWaitsec/ZB9nDdsj850+in6yTmDqnijrbPrCV1kqE7zpIpQilpO9gC6lLjJyJ3woQgDmYDpgvFnMsGKnXqJKwWEzX30mLuF4HBbr00XTJ9l6T8ajHSWGRI6baWQLOLbI4BZ2p286do76StceXWfrCPJqlMfb+KZzxbMpuxhsXSwfXEHTDdMCi7yrfmgWtEJWcrvULGsFFH+NU5/T+UaJY7IqrHnfG5meT5p1ZzrmBXKhAejvw0ZeST4BarZZUq9X/ADz5ejvXarVfqFar3yR9Q/+oVqu99K0WAf/XRcb8xqDrw+9/EeZWYfc4fPd9CE3DuuMVXQB/6Xuv2Olk3sL5wN4L2vZlC4AoSb1HLxClFN2PPUO8kno++V84hvuBPWj9Llr/mYmi8z03oX1jHhKFede5pzjROt14iSiGXpAJpBkZGRlvUMzzDJhtMA5wtjuyQdLpThkbw2XlpxlXHs3WCDshvUUP3dbou+3c9kwqVliPHUN200tLugI+sBW749PSdWSiiDWB4/kopVhOdGwSEILmetOX8U6HJAjpa/dYy9mMhWkTlm13V/iGEiQJ7N1hoyHQpip81Poma56iIGLcm+68Is857kUsfn4OFERhwuLn5pj6ge1X5NgZGZsRXQq+b6/kS7MJti54+8zVrcLLjeewB228JQ9pSvrO4x98Lm7qEzy9CIlSFC3BZOEqBJpx3bFJ884s59xALlQgbQAzwP5XrZ8B6hdygFqt9idnWfexCzz/G4+vH4CT6/6cz59I//ZObWxMr+JixFEAgvhlcRQgPtU+97ENDeve8dc/5pt3wexyKo7ePAkDxYuLKSMjIyPjumEzekGt80Xgo8DPrD9WQggN+GlOz4rJuPZcdn6acWXRTEljtsPqvjpmQWfiHNWjAHockx8wWHoxQDclg33ptHnhhUihgxToKiHpxfRaIUkCucRDAe3IAGDPygIzzZiWZbF3zsctpMd45v5dOGsvIOKEJx/Yzu2ahJKD9SP3M/ziEowUkZMXL7KcFSEQQqDU+vT/i0yfMzKuR0YLgu+9+dpYm2mmZPsHZ+gtepglAyNvXPQxdvYJfvg2WOoKZkqQM0+Luv6an1apFy7+uBnXN5s078xyzg3kQgXSjwO/Va1Wfwr4yvq6+0mrP//g9XauVqvfdb7/r9Vqn7zAON44SHn+x9chwtIxdvQRHlwFwNh7BdoU7hiD/+Xb04rbgeLLdgQZGRkZGRnXkJ8C/k4IcTdgkU7f3gOUSPOhjI3hsvLTjCvP4r415h9fxlcS2Q144XdfZOKtZ58ppPImq3NdgpOrBJpGszdKGZArPSgVSTSJFiX4B1aQUrGltYSTpBWiZittYWAUdPrmWwy22yRSEo2m1UCzw/20PnxfevcrBVGi0KVAVFzE3Ve2IEGzNYa+dZzlR08hHY2hd15AAUBGRsZFIQ1J7jKtK0ZygpFX9Waa+8I8y99YAQET3zJ2SdWpGRlXmCzn3EAuVCD9KVIH299+xT4h8BvAv7qA/f/4HOtfEu1vvM46b9qZVkaeXIHdExfUBOl6wPngHoz9KwhLQ9/Wd2UOmrPTv4yMjIyMNzRqc5rlo5R6TghxC/BPAB+wSc3yf00pdWpDg7uxudz8NOMK03ixiSd1RJwQC0Fj6dxegdHJOsmJeXSRXvn+V/cD96GiiFyjS6xJ9DAikglJkFCI2ijSgoJClM5SEncOYz2/iEgkSoZEtw4BcM+o4K+PpFb/9w4LdHl1v1vKt/dRvv0K5b0ZGRnXhDhIUnEUQMHiY8uZQHoRhJ0IzZLIi511uonYjHlnlnNuLOcUSKvV6oPAV2q1WrTuGfoT1Wr1XwPb1jc5VKvVuufa/5XUarUzrppqtaoDdwD/FvjZS4r8escy4MMPbXQUVxyhSYybr0DlaEZGRkbGDccmneoEgFJqHvi5jY7jRudK5qcZV56RewZI1HCdtQAAIABJREFUBmZR3QCkpP+Oc4uGRhJhEtBWEqkgt26tFtgmMRoCRWQaJBWBUzJRmsKIIgBiLa2tGJ6x6TgJKoyRhmB4W1phVrYgSARxAv1Zp+qMjIyzIHWBZmvEXgyQTbG/QJRSzH7mJPX9DTRHY+Y7p3BHrs8eIZs178xyzo3jfBWkfwuMAovVavUwcHetVlsBnr3ck9ZqtQh4vFqt/gzpKP9tl3vMG5nO0yt0nlzGGHQovWMCaVy/ozgZGRkZGTcum3Ek/yXWR/N/BNgK/KBS6pQQ4n3AMaVU1hDo2nHV8tOMy6dvV5kHf3w7z/3lKcpjNm/+yZvPua2Y6mdFWLQ7AikSnLEBxoCo7BK1IgQCJQRyqkKSQNfKoYsQJUA4JgClyRJRwSCJFFIXlLakXvWfP6Zw1+9y/uaY4k2joF3lKtKMjDcySinEG8zqTEjBzHduYf7vF5GmxthDw6+/Uwa9BY/6/gYAcS9m8bFlpt87ucFRXRqbNe/Mcs6N43wC6Rqpyf0iMA1cDdWtzukR/4xLIFjocuQPj1DvQdluIW2N0iOZ91FGRkZGxvXHZh3JF0K8A/hz4LPAI6QdRiHNYT4KvG9jIrshuRb5acYlksQKvRuxfVwicoJwLcB0z367sfjkEkudMhgxQsDhZ+BmoKz5LAkNkSiUUPRbIVKXmH7CehN74tSKFLlnHOPhnYiTa6ixCtre1LLK0aGxvo2pQaaNZmRcGkEj4MifHsdf8+nbW2HiW8au6vni5S7enx9A+THW22cwdlxd6wp31GXrd09f1XO80dAsmZrbrCdtun39uiVuxrwzyzk3lvMJpH8C/F21Wj1F+tmpVavV+Gwb1mq1rec7SbVavfNVqwTp6P9PA5kCfhmcnAt4akGhFIgmGPM+pY0OKiMjIyMj4xLYrCP5wC8CP6mU+nUhROsV678A/K8bE9INyxXLTzOuPN2TbfRPfZXBpRUSQ2etIsh9/96zbqubGoEvSJSBAuxWeqtaVB5N3yCWGnoQ4pgJQTdi2Swz2GmgBCzlygD8/+zdd3xkV3nw8d+504t6XWl7sXftLV77uGFjg40bYEIC2BBiagIkBEgIL0neN5BKkvdDGqGEkoTQ3wRMC6baGLCJ27HX9tq79q63aItW0qqX6XPP+8ed3dVqJe1IGmlG0vP9fPTZO3dueWalmXnuuec8J3ZJA2PXbiDblSDQEiW6sxGAX92kuOcgZF24eY1acj3fhFgo3Y+cJN3v3W3o3z1A7ZYa4itj59lr9lLfeZ7c0WEAkv+1B/8fvgi1iGtcLkWhuhANO+o5fm8n4aYwLdcs3vJ6FZp3Ss5ZRtM1kL4Lr+V6E/APwOeBkWm2n47BS2In/gU+DLxtlscUQGd9FcnmOOHuUfIBHycvaObCcgclhBBCzEIl3skv2Ap8f5L1/YDMzLKwSpmfihILDA4R6vUmPXGyOUKHj+C9fc6VzIDKQTSVwyrIhLxeSKPVdTjdw6i8xVGQqK4jXBUkbPOknBAAEcf1zhH20/LOLbhjOZyY/3RDaEtM8bZtFXnhK8SioiZ0v3Z88/u+sqkz97tsNg95F6SBtKJkx3IM7Bkk3BgGC31PDtB6TXO5w5qVCs07JecsoykbSI0xFrgHQGu9A/h7Y8xsE9B1Ex67wEljTGqWx1v08skcgz88Sn4oQ9WLWohcUDur46ytd/jpKzbhG0xh40FetnlxFZdOPNtPpjNBZEstoZXxcocjhBCirCq2QaMfaAcOT1h/KXBswaNZxkqcn4oSC62oonpTNcmuJP6wj9glU180504kiCWyKNe7RFWj3oz3+doIx+N50gE/0UyOzW1R8ukc4aYQ+V4LCmIrQqePo5TCF19c+a8Qi0XL1c2k+9Kk+tM0bKsjuiI6r+cLvWwdyW/sxWbzhF+6FhWarj+XKIfMYAY3455+nOpdzE06FZl3Ss5ZRkV94hhj3jrH86yhMOPo+JWF2exfZIz5xRyPv+gM/vAoiae8O+yZY2Os+L1ts0ru2qsUb7/ER8dwjDXV0BavyDf5pMae7qP/m4cAGH2kh5Z3biHQNG4GPNeF/zawrxNWNcJrroKAfEkKIcRSVaF38gG+CnxUKXUHXph+pdT1wN/h9WAUZVCC/FSUWlMNkbdeS8QcgIYquGX7lJvGWsKnG0cBgtZbfioc4xvXrifv8xHMZFnbN8qOoI/ABfUo/yAoCG9tnPeXIoSAQMzPhjsn9nWax/Nd2ID/g1dD3qLCct1XiSLNYcKNIVK9aVBQu3nxFvir0LxTcs4yWqhPndMzjk5YX1N4bvFW9p0ldyR7etnmXNxEbtZ3v9viirbF1PnSWvif57A/OIB/OEiuvh6bc8l0Jc9uIN1zFHYd9JafOwaP7odrtpQnZiGEEPOuQmtBAfwJ8B9AB153gz2Ff78KfKR8YQlRgS7d4P2cR6Y7QcbvEMi5WLx6oQC717bSlT1V4zDCwbYgSincv3wx933lKI7f4VVvWzNv4QshyksFfCCdwiuWE3DYcOc6Ro+MEawOEGmJnH+nClWheafknGW0UA2k4+Y5O0sDMLZAMVSU+NUtpI+MYnMukS11+JvC5Q5p4Ty8D37yFKHhNPlnBhi9bCc01hBaPaGVN+dO/1gIIcSSUqF38rHWZoE3KqU+DOzEmzl9l7V2f3kjE6Ly7N6d4JlnktTV+bj++moikcnrB44eTzIWDaIsWAVO4RNg5/UNPP5AikwOYiHF1uubsNZyT3+QsbYA+By+3+dHbpkLscz1jcB/PghDCbh2C7z4onJHtGz4Qj5qNlWXO4w5q8S8U3LO8prXBlKt9XcLixb4stY6Pe5pH14B2v+ZzxgqVWRTDSt+fxtuIoe/Mby8ZtfsGQIgUB8idkkD/qsbCN2wCX9N8Ozttq6GZ4/A/hPQ3gBXbCxDsEIIIRZOZX4XKqXarLWd1toDwIFyxyNEpUiP5chlLbFar7tXb2+WH97dS6o3hRP04XPgppu9Ovu5nCWbtacbTCMX1oBS2MLb3lXe+rdeHeHe4w7Hhy2XtTncdEGhK9l//ZKWjhMoYPRoD1x99YK+ViFEhfnJU6evK7nvabholVfaQ4iiVV7eKTlnec13D9K+wr8KGACS457LAA8Cn5vnGCqWLxbAF1uG4we2r4HdHZDL419bj/+2TRAJnbud3wdvvN6rRerI7IVCCLHUVeKd/IJjSqkXgJ+d+rHWdpY1IiHKrOfQGM98uxM3k6f9yga2vKSJk0eTpDpGwIJLlmOPD8DNtfR2p3ngcx1khzOsuLyBa1/bCllgXAcBFfCWn+1y2dmk2FSrqAvB/l7LhjqXuqMnSOS9SZrih46X6VULISqGreCsQSwKFfoXJDlnGRXdQKq13ga8E9gAvM0Yc0Jr/Wqgwxiza7J9ThXP11ofBv7OGLMsh9MvJu6hXvJ3PwnZPL6XX4yzY2XpT7KuBX7nVm9YxKpGCAen314aR4UQYlmo0EQVYBPwksLP3wIrxyWv91trv1a2yJa52eSnojT2fesYY4UJRw8dH2XdZbU0xhS1uRTJoRwq5LA67pWQeuru42Q6hgHo/MlxunUNDdvqccIObsoroVS13iu11DkKDx13yeSgKgyvSVscv49jNfUkUy4OlmBdbJKIhBDLysu2Q+/wmSH20ntUzFCF5p2Sc5ZRUQ2kWuubge8CPwBuAE5V4t0AvAV49XT7G2P+fPYhioXk3vMsjHqVEPLf3Y3a2obyzUMDZX2V9yOEEEIUVGixfMYNc/o3AKXUZuCDeDnQ2wFJVstgrvmpmJt8/5mBYXYki826VDUGaT3WS9eIJWrztN/uDa93svkzO1rvcaAxRN3mWkaPjOL4Fa1XtwAw5kLfqEsqa8nkHbKFXqY/2XoFnBzGooi1L95Zk4UQJdJUA+95RbmjEItYJeadknOWV7E9SP8SeL8x5lNa65Fx638G/EExB9BavxV4A7AaOKvLoDFmfZFxiIWkVCWW5RBCCLFEVWKiCqCUcgANvBTvjv41eGWEvoKXC4nymHN+KmZvzVUNHBzJks9bGtbHCFUHOHh/F/fWNHKyNYzftfh+PsjmN8G2m5v55UPdZEaztF7RSNPGOOnBDGG/xY36cBxFIO81ouazltYaHxaLoxTpjMW6lrT1YX0xrIJgrswvXgghxKJXiXmn5JzlVWwD6Vbg+5Os7wfqz7ez1vp/AX8MfAa4DvgUsLGw/HdFxiAWgHP7NvJ374Ksi+/lF6FkeLsQQogFUqFDnQAGgRTwPbw79++y1naUNyTBHPNTMTerb20n2hwhn8rRsKMex+9wJOPjZCwCFnI+xd6QN2w+dWyMNZfW4GZdfBFFZiiDyrskAgGeao0TwdLqep8AV69yeOqYpX/UsrJOsbnRQTmKLcf66Ms5KGBlxgJ15XvxQoh5k9w3iJvKE72oDuWXa9FKlOxJMbB3kGBNkIYddYt2wukKzTsl5yyjYhtI+4F24PCE9ZcCx4rY/7eAdxhjvqG1/l3gE8aYg1rrDwFrig22WFrrC4AvAA14re1vMsbsn7DNnwG/A5wqePtLY8y7Sx3LYuOsqcd5/43lDkMIIcQyZCs3v34a727+lUACGFNKjVpr+6bfTcyzueanYg4cv0PzFY1nrdt0WR2Be5PkhjPgc1h3bWEG+2Qex+/gFBo78qk8qjrAT2INDA/mwAfRYJRLgHaVo+Gx45CCtkYfdbesRblw7UAfe0YcFJZLVvgW+uUKIRbA4L3HGHmwC4CxJ3ppfsuFZY5ITJRL5Dj49cPk04Ve/6k8LVc1lTmq2anQvFNyzjIq9pbMV4GPaq1X4jW0+7XW1+P1/vxiEfuvBB4tLCeB6sLy14DXFB9u0T4NfNIYcwHwSbyeq5P5ojHmksLPsm8cFUIIIcpKqXN/KoC19lq87mrvBQaA3wOOKqWeVkp9rKzBLW9zzU9Fia1qDfL+32xh54vrue2VjbztzgYAmq9oxPEr3FSO6k1VRFojDHSlSSRy+PM5/NkcJ7pSADx6fx8rEkm2uknCPaPsfmIIqxQjozk2d/dzYfcAvWNuOV+mEGKeJPcMnl5OHx4hn5R6GpUmPZA53TgKkOxOTrN1hZOcU0xQbAPpnwCHgA4gDuwBfgo8CHykiP27gFO3mDuAqwvLGylxz2atdTNez4FTxWu/BlyqtV6ctzWEEEKIZcI65/5UCmtt0lp7H/AJvFJB3wQ2A79b1sCWt7nmp2IerDjaz3WP7GPn04fx5b2GzEAuR8vICC3DI9SPJQCosXlWjoyiAg7K77ClfwCAcPzs3qHR6gD5vMtIfZTBlTUMrKphtCa8sC9KCLEggitjp5f9DWGcsPQWrzThpjChujNTylRvrJ5m68omOaeYqKgh9saYLPBGrfWHgZ14Dau7Jg5bn8ZPgVcBT+DNxvWPWus78Boy/2vGUU9vFXDcGJMvxJ7XWncW1p+csO3rCzOgdgF/aox5aOLBtNbvAN4B0NraWuJQhRBCCHFKJSWn4yml7sArlP9S4AK8vOEXwHuQgvllU4L8VJTY4PNDmA89QX4khfL7yKXyXPlXlzH8ww76Hukhn3ZJnkgQubad6KoYr4kPYToGiZPnmtevBOC6lzfT2ZOl51iKzTtruGhbFQBbL6ni2acVSsGVly/eC3IhxNTqX7WGQHMEN5Wj6srmRVvbcinzBR02vGE9I4dGCNYEibVFyx3SrFVi3ik5Z3kVW4MUAGPMAeDALM7zDgq9VY0xn9ZaD+DNxnU3Uw9/n2+fBj5ijMlqrW8CvqO13mKMOau2gzHms8BnAe66664KreMrhBBCLH7WqdgLoX8Cfl7492fW2ufLHI8YZw75qSixnoe6yHcPY603RKz3viPwV5cxdGSMfNrrTZrsSpIayRFpVOxqaKZ7KEmvX9EUraEF6EwojmxrJ3+x5bmA4iUZS1VQcdd713HoiQF8fsWanTJBkxCL3s+fhX2dsKoBbr4EHK83efW10imp0vnDPuq21JY7jDmr0LxTcs4yKqqBVGv9z9M9b4x573kOsRI4Om77/wT+U2ut8Hp2HikmjiIdBdq11r5C71Ef0Db+/IUYusYt/0RrfRRvNtSflzAWIYQQQhTJVmhPEWttW7ljEOcqQX4q5iA7lqPz/hPkk3mar2wivjqG2zsMrovFm0zJGRoBwLm0heOHsyQzluYVQQJNEXp6s+w+arEEIQc/fjTJi98Be/stY71pcmM5snVBDg8F2dYETsBhw5UNZX7VQoiSeO4Y+e/sIjeQxVfThb+hCi7fVO6oxDJTiXmn5JzlVWwP0m0THgfwaiD4gF1F7H8IWAH0TFhfX3iuZMVFjDE9WusngTcAXy78u8sYc9bweq11uzHmeGH5EmAtIK3zQogZcYfTADjVoTJHIsTiV4lDnURFm2t+Kubg+L2dDB/wGkAT3Um2vONC/G01xBkjl/fhqDz+Oq+n58iqBo5scMkm8yRWR9kZD9LVnyaBQyybwyroyXkfAFW9Yww9PwZA6sQY1VvrAPmOFWIpyXcMMrZrGGstCkX0in78l5c7KrHcSN4pJiq2BulLJ67TWofx6ok+UMQhFJNPxhQHUsXEMEPvAr5QqEk1ALwJQGv9feDDxhgD/LXW+jIgD2SAu8b3KhVCiPNJ//IoqZ8cRClF6Ob1hK5eWe6QhFjUbOXdyBcVrAT5qZiD7Ej29LKbcXHTeWoubmSsvYVg3zDZoJ+qazcCcOxomoNHs2QzLo02zeioS5uTYyQeYiwUwFWKuoB3rHWDw1x+7CTdWR+bonmqh8PQLg2kQiwl+VgVNhqCsRQ2HCBXVTOz2n9ClIDknWKiWX8OGWNSWuu/Bn6IV8/zHOOGPlngb7TWiXFP+4ArgCdnG8M0sT0HXDnJ+pePW35zqc8rhFhe0j/rAMBaS/pnHdJAKsQcjR0dK3cIYpErJj8VpdF0eSN7vn6EXMZl9VWNBKoCVK2MsfLG1Qx0pghGHNbf6NUS7PhpF8lBr69E74EcgweHWL2lmpcl+jg+4HoXBRd4vU2zYzlWdQ6yylqU38HmZQoAIZYa36YG1JWbsGNpVDiAf2tLuUMSy5DknWKiud6oacTrBTqVU0OfFLAFr6fmKRm8We3/bo4xCCFEWah4EDuQBMCJB8scjRCLX3jtdCmFEEU7X34qSqAzF+RAayP5tEsmGGe1awk3hLjg19fT//QAwdogK67zGkh9faNUD4HrOARsjlxfGgWs3xSlYf8IvpBD47oYAKH6EHUX15IbyxGsDeIEZAykEEuNryVO7B2Xkjs4gG9lNf7VNeUOSSxDkneKiYqdpOn9E1YpvJqibwS+P9V+p4Y+aa0/D7zPGDM8yziFEGLejD3ZS3LvIMH2GFUvbkUVWbA7+vqLSP3oICgI37JhnqMUYhmowGL5SqkA3kSPN1prny13POKM2eanojSeeirBkW5vZvqBpxNcfXWcWMxH/dY66reePct8KG8JZXOAwm9dIjUBlF8RXxkn1uY1jIbrvWH0jZfU89hzKTpHLVvb/FRvqFrQ1yWEWBi+1ji+VmmgEmVUYXmn5JzlV2wP0vdMeOwCJ4HPA39zvp2NMW8d/1hrvRrvzv5eY4yMmxFClE26Y4T+bx8GIPn8IE7UT1w3FbWvryVO7E3b5zE6UQkSQ1k6nhxCKcXaS2sIx6VK1nypxFpQ1tqsUirL5LXURXnNKT8Vc5PJnnlL5HJgp3mHpIezBHNeYyoKhjsTrHlRE83XtXDwe8cIxgOseInX23RvOsATF6/C5iwng4oL04rmwsdu15jFp6ApWoEfFkIIIRaVSss7Jecsv2InaVo3m4Nrre8E6o0x/zJu3b8A7yg83Ku1vuXUbPJCCLHQcgPpsx/3p6fYUixXe+7vJTHkTUaSGMpy6e2tZY5o6bIVdid/nI8Df6yUequ1NlfuYIRntvnpRFprDWwAvmeMGdNax4C0MUZ+19O4+OIwuaxLPg+tKwJEIlMPhQ8OjeHYEFYpAtksajiNm3N5eneCA8EqAnmI7hlhy7Uhjo9alAIVULgWuhPQHIMfHHR55IR3zfiyNQ7XrqzYzwshhBCLQIXmnZJzltF8d4N5D/ClUw+01i8D3gl8CNgLfKSw/K55jkMIISYVvqAWf12I3EAaJ+wnuqO+3CGJCpMaOZObpEYlT5lP830nvzB0qdFae2KGu74YuB44rpR6Bjirqr+19lUlClEsIK11C/AdvElDLbAJOAj8A5AC3lei83wSuBFIA6N4ZadMKY5dTjt3xojHfWQylvXrQ/h83hv4yO4h9v/PAFWNQS69vRV/0CEylqI2mcL1OfhzOZQDw8M5nujIki90LH1oT5ot18LmeoXpAtdaogHFmmpv+dGuM+d++ISVBlIhhBBzUqF5p+ScZTRlA+m4GejPyxjz3imeuhB4eNzjXwF+bIz5SOEcKeATxZ5HCCFKzRf10/Kui8h2J/E3hPDFAuUOSVSY9i1VHH12+PSymD/zlagqpV4F/DWwHnhBKfWvwMetnW5Q8Fl6gbvnJzoxEyXKT0/5R6AbaACOjFv/dbweHKXyA+D3jDFZrfUrgf/E67G6qPl8is2bI2etG+pO8cAXjuIWZp63Llz5ujZ8jsKXy+ECwXQWn7XkQ36ysSDOiDeHa7reO9b6WsVvbYfuhGJdDVSHvA+GmpBlMOWdpy68MK9RCCHE0lWheafknGU0XQ/SbdM8N950v+Q4MDDu8YvwksJTngVkrKIQoqyckI/Q6vktEu/mLeneFP54gEBMalguJut0LU3rowDE64NljmZpm+tQJ6XUVcBn8W7O/q61NqOUWgf8X+C7wKfxbtb+E5BSSv2rtdY9b1zWvvV824gFU4r89JQbgRuNMQPeKPvTDgCrZxrYVIwx3xv38CFgpdbaMcac929vsRnqSdM/kGdkzMXvh/rjSQBG/SE6WuuxShFJZ9DxAPVRhf+KFh57cgxfyOHdV5z5Hl4RV6yY8LX8xi2K+4+Az4GXrZHeo0IIIeamEvNOyTnLa8qr9FMz0M/RMeBi4IjWuhovqR1/N78Bb6iREEIsWW7ecujuw4wdS+AEHNa+ejXxVbFyhyVmQBpGF8Zs7+QrpZxCwvn7wBpgP95s5h3Am4Ec8Hlr7SHgn5RSm4A34SW0T8/gPKdrVVprx5RSMSAtNaIWTony01MiQGaS9U14Q+znw+8C90zVOKq1fgeFWv2trYuvD0E2HuTYKJB0UUpx0Hoz0/dXRXEzXo3SZCREb1+e9iykrWLNhggBv6Jj0E7bLN0UVdyxWRpGhRBClEYl552Sc5bHfHdj+jrwz1rrvwFuBU5w9pB7DTw3zzEIIURZJU4kGDuWAMDNuvQ92S8NpEtUpnOM3FCG8IZqnKCv3OEsOpN1+VNKnW4wKvistfazZ+1nrauUuh3vpuuXgK3ASrxEdR1wHNivlFKF4U3fAy4DrgKeHrd+UkqpBalVKRbcL4C3AP+78NhqrX3AHwL3FXsQrfUTTN2012KMyRe2ez3w68B1Ux3LGPNZvN4o3HXXXYtuFtvnT+R5uLmJqkSaVMBPRzLIe4BolZ9kVwZf3pIN+WnYGCdvIbOvn2DnCMrnkNYNsL263C9BCCHEMjHxS7aYnBPmN++UnLO8im4g1VpfALwWLwE8qyuNMeZtU+z2l3h/KH8PdAG/cSpJLHgDcM9MAhZCiMUmEA+gHIV1ve/BQLXUOV2Kxp7qo//bh8FagiuiNL99M8o/9azO4lyTDXUqJKbnJKeTuAN4BNiNV+D+VPe7XmCrtTavlHLwks19eEntRYVtFNMPyV6oWpVihmaZn57yQeDnWuvLgRBevnoxUANcU2wMxphLi4jzV/EmJ73RGNNd7LEr2UjHKE98ah/psTwX/Wo7K29sI+FCTzxMMuAn6yh8Ae9SozWfhIEErqMIDeWprfETT6a55vgxfqzqiOfzXH+4g+IrKAghhBBzMzHvnEHOCfOXd0rOWUZFNZBqrV+BVyh2F17L92N43X1DwANT7WeMSeJ1JZ7q+VIOkxJCiIoUqg2y+hUr6Xt6gFBdkNYXNZc7JDEPEk/3QeFmcOZEgmxPkmCb9BSeiTkMdXopXtL528BavKSzrfD0IeBNSin/uGFJ3cAwUAteT4DznOJG4EZr7YA6O5kuaa1KMTOzzU9PMcbs0Vpvw/u7SQNhvAuQTxpjZjLj7PnifCVez4+bjDGHS3Xccvv5x/az+4Uc1kLHZ47y5ssbuWxjiKqaPH25AAFleclGb9tEV4pwJoMCUIou00fz1lr6AyFCWKxVjERC5Xw5QgghlpkKzTsl5yyjYnuQ/gXw58aYv9FajwB3AZ143Ykfmq/ghBBiqajZVE3NJhk6uJT5miP07PZmu2+p8+GrlYv9mZpNsXylVBC4HfiytXYUeEYpNQRsVEpdATyKdxd+C95dfqy1o0qpZuCYUipgrc2e5zTlqFUpzm/O+akxpgv40/kLEYDP4/39fGPcZFA3GmP65vm88+qFQxmySe8aryujGOpJsW5jNR95eZh7nsmwssbht1/mTXCXcXxYpbCAdRSB+jBDys/zW9fgOzwEQR+Pb67nsjK+HiGEEMtLheadknOWUbENpBdyZvb5LBA1xqS01n+BN0T+H+YjOCGEEGIh5V2Lz5nd7eSfrlrBsbUOgdEM7S9q4tXR+S7zvfTMsuDiCrwJID+glPo0cAlwKXA18B68O/pHgNcrpfZba1NKqQjenf8vF9E4CpPUqlRKzbhWpSi5OeWnWuupaoFavIuQA8aY/rkGaYxpmusxKlE+5KM37cdaiPotvpBXd/mm7SFu2n72DaLsmhqyiTy+vEsuGiRdEybsh0hzlExDBIDaapmASQghxMKp0LxTcs4yKvbqbQRv2BF4Ey1tBJ4p7F83D3EJIYQQC6ZnxOUrT2QYTsGVa3zcunnmdWL3Djnkd3jlh4Z8ileXOshlYDZ38oFG4EngXcBevCHS3UA98AfW2i6wqmOzAAAgAElEQVSl1MeBDwB+pdSX8Arc7wPuL/IcHwR+rpSaU61KUXJzzU9/xpnro1N/fOMfu1rr7wJ3GWPGShHwUtLdGqcbFwWoADhx73Nz+MAIfbsLJWWuacbxO9Rc3khPb4q8BV/ET+v2WsJ+xRs2wwPHIRaAW9dKA6kQQoiFU6F5p+ScZVRsA+kjwLXAHrw78n+vtd4B/CoyxF4IIcQid/8LOYZSXrvIwx05drb7aKma2QRLK+PQ4Y2wpz1e6giXidm1jzwF3AyMWmszAEqpUeBtQFVhm08CPXjJ7Lvx8pn/ba19vpgTWGv3KKW2Ab/DhFqV1tqS1aoUMzbX/PQVwEfxJk96pLDuSuCP8Ybdu3iTJfwtXq8QMU7Nujhh0rg5l2hdAKUU6cEMHd87is1bRgClFCuua+HWd67jUyeyDPTnuObmRla0ee3a62oV62qlYVQIISZjrUXNrhFPFKMC807JOcur2AbS9wOnLvf+DO8X/xq8VvD3lz4sIYQQYuH4JrSF+maRML1hi+LhTm/5qjZJZmdjNsXyCwXw+wGUUk6h8P1hvLvuVYVtUsCXlFLfsdYOzyo2a7uAD89mXzFv5pqf/hXwPmPM+CFrB7XWJ4H/a4y5TGudx5s1VhpIJ7j5shgH9ydJ5eHqlT5qagOMHh3D5s8MWswMeWXU7v1RH4erq7DV8NMXclzbk6KuOTzVoYUQYtlLP3CE9M86ULEA0ddfjK+t6vw7iRmp1LxTcs7yKaqB1BhzcNxyAm+2rmlprX+t2CCMMd8sdlshhBCi1G66IMBQyjKQsLxorZ/G+Mx6jwKE/YqXrJaG0bmY5VCnM/ufmRX0PmCntdZOeH5WjaMASqkVePnPRYVVe4BPW2s7Z3tMMTezyU8nuAg4Psn645z5Pe8GWmcV4BLnH8rw5stDhdrNlkwyT3RFhOiKCIkTSZRfUb/dq3TQ0Z2l/0SKlAv1UUXPQJ665jK/ACGEqFDucJrUfYcAsMNpUj8+SOwtO8oc1dJTqXmn5JzlU1QDqdb623gzgv63MWayGbUm840it7OAr8hthRBCiJKriSjefmXpZ5231nJvh+WFQVhbDbesUzhKwdFeyLuwpglk6NRpc01UTx/Hu7tfMkqpm4DvAEc5MxT7DrwC/a+21v64lOcTxZllfjreHuD/aK1/0xiTLhwzhDcxwp7CNquArlLEu9T4g9771ecoHF/hx+/QdsMKeh7tJboiQtUar4NvaiTH0yoCDtSk89T6vWvKnGt5YcCrQbpKJmkSQgiP8kqUnG5vm83QJnFelZh3Ss5ZXsUOsU8AXwCyWuu7gS8ZY34+3Q7GmJl3vxFCCCGWkGd74ZfHveS2ewyao4rLnnkKHtzrbXDpenjVFWWMsMJUbmPxPwP/CrxvfO8ApdTHgI8BW8oV2DI34/x0gt8B/hs4rrV+prBuK17t0VcWHq8HPlWieJeUjVfVs/+X/WTTedZcUoM/6JAdy3Ho7g7yqTzD+4dRStF0WQMnDia4qC+Lk7Nkw36ePRynaX2cLz1r6Rj23lK3rnOkPIkQQgBOVYjwyzeSvv8wKh4kfOuGcoe0NFVm3ik5ZxkVO8T+17XWMbyi978O/ERrfQL4GvBlY8wz0x5ACCGEWIYSuUkeP3HwzIpdh+D2yys1QVt4lfvfsBb4xMShU3hF+H9r4cMRMPf81BjziNZ6HfAbwIWF1V8t/FxV2OaL8xX/YheK+YhfUE0mbYk1ez3w031p8qn86W3GjidouqyBpp4hIn0ZLGAdRay/hqF0w+nGUYDdvVYaSIUQoiB4eRvBy9vKHcbSVplfOWuRnLNsiu1BijFmDPgy8GWtdRNwJ96sXB8o5jhaaz9wBbAaCE44dsmST631BXi9CRqAPuBNxpj9E7bx4bXM34o3xP9vjTH/WqoYhBBCCIBtjfB4t6J7zNIYUexsBpqqoeOkt0FjtTSOjmOdiv2/MMA2vMl/xtsG7Fr4cMQpc81PC/t/BkBr3Q68FW+G2rVICahpPfHQME/9tA83Z3n+gji3v6GZcHOYQFWA7EgWgOr13hD7VYkEPaNZsj6HmlSG+jo/sQDEg4rRjHcN2BIt20sRQgixDFVo3ik5ZxkV3UB6itY6DNwA3AJcgFcb4Xz7bMYbwrQOr50+Xzh3FkgDpbw7/2ngk8aYL2utfwMv6b1hwjZvBDYCm/AaUndpre81xhwuYRxCCCGWuUhA8c4dMJpRxAJerT772qsZ+q9d2Eye6tftlBaYcWyF3srHG2L9j0qpTcDDhXVX4RXQ/yOl1KWnNrTWPlGG+Ja92eSnhf18wK8AbwduBp7Gyx2/Pj+RLh0HHuqnfyhPDkX2qSHGbqunqj7AxtevY/jgCMHa4OkapLTEqO86CVmLDftJKD9Bn+LNF8MjJyAegGvaK/b9L4QQYgmq0LxTcs4yKnaSJgXchNew+Gq8Bs6vAzcaYx4o4hD/BDwOXIJX6P4SoAb4F+BPZh72lHE2A5cWYgVviNUntNZNxpiT4za9E/icMcYFThaK/L8O+GipYhFCCCEAHKWoHjf/0wvPpDjhXwV+qDVJtt9aVb7gKkypiuXPg68U/v3raZ4DmXhyQc0lP9VaXwj8JvAmYAxvWP0twF3GmD3T7Ss8PQmXvsOjOLk8wy1xAj5v4qVAVYCGHfVnbbvqtnae70mhsnloibLhsloAmqKKV26o2Pe9EEKIJaxC807JOcuo2B6kJ4Bq4AfAW4B7Zjhb6OXA9caYMa21C/iNMU9orT8IfBzYPoNjTWcVcNwYkwcwxuS11p2F9eMbSFcDHeMeHylscw6t9TuAdwC0traWKEwhhBDFSgxlGe3LUN0cIhyf8cCHkhoczHH4cIZY1GHDxhDOLIbmDBxPnTled4p81sUXKG5ew8RIDteFeE15/x/mTWUOdQJvBIyoPLPKT7XWD+BNxnQ3cMepiZ201n84f6EuPa37jtN4sJeccoh0w1huPeEptr3i5S08diDFyWGXW66OE68NTrGlEEIIsUAqM++UnLOMir3C+hDwdWPM4CzPo/BmGgWvobIdeB44hjfUvWIZYz4LfBbgrrvumlgoVwghxDwaOZnmqR/14OYtgaDDJa9sJVJVnsbBdMrlgQdGyWa9XlLZrOWiiyMzPk51c4jUIW/2pnh9sOjG0Y7nxtj3xDAAa7bEuGBn9YzPXekqdKgT1tqO828lymC2+enVeJMdfNYY82zpw1rccqk8WIs/Mv1nbUs6QSKbwrqWoD9AJJVhwjQDp9199wn2HUij0nm+8XPYcWWC5pVSdFQIIUT5VGLeKTlneRU7i/3n5nieZ4AdwEHgUeAPtdZ5vFm4Xpjjscc7CrRrrX2F3qM+oI1z61AdAdYAjxUeT+xRKoQQogL0Hk3i5r17U9mMy2BnisiF8Xk5VzadJzmcI1oTwB88t9FyLOGebhwFrzfpbFxwTT1VjUHyOZcVM3gtHXvHTi8feS7Bxh1Vs+rBWtGW2MsR82sO+enleMPrH9RaH8arhf+1UsW1mPXvHuD4fSew1rLiulaaLmuYctt6cmT7krhA1M0Tapyq/yjs2jVGYKDQe/5wlj37pYFUCCFEmUneKSYortvK3H2EM39+f4LXIHk/XjH895XqJMaYHuBJ4A2FVW8Adk2oPwpefarf0lo7hRlPXw18o1RxCCGEKI14/ZneSEopYvWBeTlPcjjL49/uYte3O3n8G8fJJPPnbFNd7aO62iv1o1CsXDm7IaKOT9F+URWrt9cQCBVfOigcPbNtKOosvcZRvFpQE3+EKDVjzC5jzLuBFcA/AK/Cu5nuAK/QWteVM75y6vplD9a1YKHrwW6snXrwVO+IwqmLEKwOMxSJkewYAeDpbpd/fzLPd553yRRucMWqgqcvBAI+RaAmNMVRhRBCiIUhOaeYaEHGKRpjfjRu+SCwRWtdDwwYY0o9bP1dwBe01h8GBvCK76O1/j7wYWOMAb4EXAnsL+zzF8aYQyWOQwghxBw1rY1i3QaGT2aobw9T3TQ/F9U9BxMkDwyS2z9ABugMZFj7ug1nbeP3K66/vpru7izRqEN9/cIO9d92TQ37nxzBzcOG7fPTi7bchrpS599IiBIxxqTwcsIvaa034vUq/X3gr7TWPzXG3FbWAMvAF/GRS3i9431hH2qaC8ZIW4yjvVlcFLVRH8EVMXoTlo8+4nJ8xBLxQ8Z1eN0WH7fe2ca3PpXBpnJUXVjLFTtiC/WShBBCiElJ3ikmWpCrO631vwPvM8aMnFpnjOnXWse01h83xrytVOcyxjyH1/g5cf3Lxy3ngd8u1TmFEELMn+b1MZrXz+/FdLjKT+7QEN6EkODs6oQJDaQAgcDse47ORuJEAjdnia2MEon72X7t0u7YVtUuQ25FeRhjXgD+SGv9f4BXAiXLTReT1betpPP+E1jXsuL66Scn7blmHU/09ZBDsWplkJtiAQ6csBwZsmRcyOTh0ROW122BF++MseXvLqRvOM+G9iD+JdgDXgghxOIieaeYaKG6v7wZ+CNgZML6CF4Pz2WZhAohxHKW3d1Dvi9BYGszvsZ5TFD2HoOBUbh4FdRM3tDavD7K6jYfQydz1Eagbp56qs5E1//00POwVyGm7qJaVt3aXuaI5l+lDm9SSjkA1lq38LgVrxFtr7X2l+WMTZRW4Sb6dwo/y06wNkjV2jjWQqh++s/BH++3PL3B+1w6kM/yq8dTtFQHGclDfxJ8DtSP+2hvrPHTWFOeSfaEEEKIiSox75Scs7ymzVK01lHgo3g1OgPAvcB7jTG9xRy8MIxeFX7qtNbjZ7TwAa8AumcRtxBCiEUs/chxUj/w5ujLPNJJ/N0aJz77npmjGUvHkKU5qmiKjUt2HtkHP3jCW37oeXj3bRA+9zxKKTb+9sWkfngAXEv41nN7jy60gWcGzizvGaT9pjYcX+UlcqVU6po7JXQP8EPgY0qpOGCAGBBXSr3dWvvFska3zMw1PxVTO3LPUUYOjQIwcniUja9fN+W2fTnFBftP4Mu5dLXWoqJ+wOHiJkvHMMQCsCK+tD+zhBBCLF4VmndKzllG57uN++fAW4CvAEng14F/AV5X5PF78f7uLLBnkuct8KdFHksIIcQSke8YOr1sk1ncnrFzGkjzyRz5gTT+pghOYOo5BUczls884dLblSY3lOGubQ7bL632njzQdWbDkSSjj3dy4rCLL+TQ/rI2QrVnzulrjRN7y47SvMASCNWHyI569xWDtcEl3zgKQAXeyS/QwAcLy78GDAPrgDcCH8CbBV0snLnmp2IKiRPJccsJrLVT1iG9uPMkA6NpLHDR8R5i6XbcqJ9NdQ4bar3LznZpIBVCCFGpKjPvlJyzjM7XQPprwNuNMf8PQGv9FeCXWmtfYQjS+bwUr/foT4HXAP3jnssAHcaYzpmHLYQQYjHzb6onu8cbPu5UhXBaz550KNubouffn8NN5PA3hml++2Z8kbO/sp45kefnB3IMZBQDwzkSz3uNrg/0pFhbo6jeUAXrWmCf9zXjRkJ0PDJMHm82+GM/6WTD69bO8yudvdUvX0n3wyexOUvTFY3lDmdBVOJQp4I4MFhYvhn4lrU2q5T6KfDJ8oW1bM01PxVTqN5QxcCz3p969fqqaSdpajs5hH8oR04pGvJZsi40RRR3XAiPd0NDBF6yytt/JGO5e5+lPwVXrVC8qL1i3+tCCCGWiQrNOyXnLKPzNZCuAh449cAY82hhmHwbcPR8BzfG/BxAa70OODIPM9YLIYRYhII7W3Gqg7h9SfybG3GigbOeH9vVi1uYSTnXmyL1/CCxS840EiYylm89kyXvWkaz0NGZoanwXKPKk+xJeg2kV18INVHoH8Xd2E7+q2e+uvLJ8VVfipNOuziOIhCY/4TKH/XTfsOKeT9PJanQRBXgCHCNUuq/gVs401OxHkiULarla075qZjaypvavBqkrqXmgpppt80D9YkUyrVkYkFCeReAzQ2KzQ1nv5fv67AcHvIuA3582LKpzqEpWrHvdyGEEMtAheadknOW0fkaSH14PT3HyxWxH1rrS4EnjTEu0AA0aK0n3dYY88T5QxVCCLGU+DfUwxSlPn01Zw+399WePVlINg9517vYjgfg8tU+4i9kachluDiUpXp91ZmNL1rlnQ9ouryRk4/1ovyKlhc1n3VM61pSJ1P4Y34C8bMbbAH27k2yd28Sn6PQl8dob1+42eyXC1uReSoA/wB8CRgFOoBfFNZfB+wuV1DL2KzzUzE95ShqL5y+YfSUqqBi1K+wKKJ+hTvNtlkXXGvJuxDwKbLTbSyEEEIsgArNOyXnLKPzJZIK+LLWOj1uXRj4nNb6dOu1MeZVk+xrgFagp7BsC8ebyEJhvKMQQggBxC9vwh3Nkjk+RmRLHeG1VWc9XxNRXL3Gz0MdOUJ+xR06Sts1QXoe6yXSXEe4OcwzJy1PnbQ0R+GG1Qqfo1jx4haaLmtA+RS+0JmvHutaDn2zg9EjYyi/Yu2rVlO19syw/2zWsnevV5sv71r27ElKA+l8qMw7+VhrP6OUMsBq4CenZhYFDgAfKl9ky9Zc8lNRIuH6EMnDo2At/pBDpCE85bab6+CLzypGMpar22BFbAEDFUIIISZTgXmn5Jzldb4G0i9Msu7LRR57HXBy3LIQQghRFKUUNTe0T7vNLZsDXLfBT8ABv09x+LudDL8wzADQ3Znm7tpWrLXsH4CgT3G915EUf/Tcr75kT4rRI2MA2Jyl98n+sxpIfT7w+xW5nNdrNbgAQ+yXo0oc6qSUCgAPAm+y1n5r/HPW2nvKE9WyN5f8VJRITUSRrAviuhCv9pEbyhCKT35psbcfLm0B1yp8CjpHob1q0k2FEEKIBVFpeafknOU3bQOpMeatsz2wMaYDQGsdAN4NfPLUOiGEEKIUIoWGSjdvGX5h+PT63ueGsVe2nH48mJ5qEIPHH/WjfAqbLzSAVp89xN5xFFdeGefZZ5ME/IpLdkZL+CrEKXaa31G5FArjr8Mb8SIqwFzyU1E6Pp/CN5DA5wKBMKH66XrVWx7vsoxkYFMdhGTsmBBCiDKrtLxTcs7yc+ays9b6JVrrae/YG2OywO8w3ZWpEEIIMQeOTxFuOjO8s21NhBVx72sn6FNc1jL9V1CwOsCa21dRtTZO/fY6Wq9tOWeblpYAN9xQzYuvq6KqSq7u54NV5/5UiC8Av1XuIERxislPxdy5fUn8qTwqncefyJAdyU657dERODDk9Rx9rh9yrlz7CSGEKC/JOcVEMy5mr7VuBd4KvA1v6PyDRez2I+AG4N9nej4hhBCiGOt+bQ29j/ehfIqmyxpYF1R0jSlqQxAPnj/rqV5fdfbkTmLhVdhQp3FiwBuVUjcBjwNj45+01r63LFGJ02aZn4o5SBwZI5v3llODOXJjWSAy6bYdw9ASPftxa3zSTYUQQoiFUZl5p+ScZVRUA6nW2gFeCfwmcBtez9M/Bz5rjOkq4hD3AX+ttd7OJL9kY8w3ZxK0EEIIMVEg5mfFdWf3/Fwp7Z2LSgXdvZ9oC/BEYXn9hOekK1yZlCA/FXOQioU41hgh7yia0kmUM/XAtGtXKvb0WXIurIgrtjQsYKBCCCHEJCo075Scs4ymbSDVWm/ESzrfBCSBLwF/AOwBvjGD5PMThX8na+2WWeyFEELMWGIoy1BXmqrGIPGGM7XvrGtRTvkyngcP5djd5dJSpXjlZj9B/+xiSadcevtyVFf7ls2Q/kqrBXWKtfal5Y5BnFHC/FTMwf62ZvoDObCW4UAN6UiAqTqFvvYCRXPEoTsB17QrqkOV+V4XQgixfFRi3ik5Z3mdrwfpHuCLwB3GmNNDlbTWMzqJMWZOtU6FEEKI8RJDWXZ9r5t8zkUpxfZbmohl0iT/3x5sMkvohrWErl09p3NYazk6DGE/NMeKS6A6BlzufSEHQPco1IYVN2yccTUbhnvS/OAbJ8laRbwtwrUvrqaxcebHWWwqbTZRUbFKkp+K4g29MMzwCyNEWiM0XlIPwFhVlEw6g3It6UiAbNqdcn9HKV6yWt7fQgghKofknWKi811t7cUbsjSgtR42xjy9ADEJIYQQ0xrqSpPPeRfj1lr6j6fw7zqEO5YBIHXvIQKXtOLEp5tV+VyJEwnyaZf46hhff85lT69Fobhtg+KK9vPf60tm7bSPi+HmXJ78agf9x71986k8nRvCy6SBtNwRTE4p9d3pnrfWvmqhYhGA5KfzKpvKYy0EI17P9URXko7/Pop1Qe0ZxPEr6rfWcdEqH48lAuQttFVb6mqX/meUEEKIpaMS807JOctr2kzGGLNDa3058HbgF1rrI3jDmGCG9Q+01nV4yexq4KwrVmPMX8zkWEIIIZa3qsYgSims9b6KqptC4D/TgKkcBUUOs3dzLo7fofuhk+z/6gFyozniF9Wy58rNAFgs5gRc0X7+Y21qdFhf73Cw36U6pLhq9cyHxmdHc/jTWRR+LJAd8YbZnxN3fxIchVMbnvE5KlUlDnUq6JvwOADsAFYBUkd9gZUyPxVnO7FvlBceHgBg7aU1rNpaTbIvxSMjITozPmr9Lrd3p6nfCu23tLCv5xg269J4VQPBuDSQCiHEsvD0YXhkP9TF4BWXQSRU7ohmpULzTsk5y+i8mYwx5jHgMa31+4E78JJRH/BvWuuvAN80xpyY7hha66uAe4A00AQcB1YUHh8GpIFUCCFE0eINQbbf0kT/8RTVTSGsa3khVo/Kp1kdzxG5aR1ONDDtMTJDGQ7e3UFmMEPthTX0PN5LsisJQP+jJ6lZvYKhFTUA1E8+MfM5fI7irksDjGYgGvAez1SwOkBTe5gt+TR9acXqnRHWrj078dz9tYPsfXiQsANXvbKJmutXEpplrdNKUqlDnay1b51svVLq74HhBQ5HUJr8VJxrz0MD7DmQxXVhNDnAyourOBmO0EUQyDPk+jgWibIeeMZGsLesQWXzdFSHGE5DzdK5XyOEEGIy/SPwrUfAWjjeB+EgvHJxlripxLxTcs7yKvpWrzEmAfwH8B9a6wvxEtEPAR8r4jgfBb4CvA/vl3oD3kz2XwP+bcZRCyGEWPZqWsLUtIRJjeYw3zqB61rY2Ep0cxUbL6077/49j/WSGfSG5A8+P4Q7rn6eL+TjFesUT1c5RPxww9riEyilFFVzuJGuHMX6162lYd8w/qif6g1VZz3ffyzBwz/sRykYjfv5p/syBMMum+oUr9+sZtUoWzkWXeyfAR7EmzldlMEc81MxwRP7cgz0e5+FY2m4TSlCMT8N22vJjubwRXxEG71W0IbuYTp+1E3aKlauCxOdY91nIYQQi0Ai7TWOnjKaKl8sc7ao8k7JORfArCZPMsY8b4z5IF4339cVsct24BPGGAvkgZAxphv4Q+DPZhODEEIsFda1ZMdyp4eLi3MlTiQY2j+Mmz13EpBMMu81jgI2k2ds7wCZk8nzHtPxn/0VuP6OtVRvqCLSHKHtuhY27KjhtVscXrHJIRJQZIYyjHSMkk/nS/OipnDS9LL/KwcZemGYaNvZXVdzyRzPf24f2cE0ydEcB1N+xvxeG9D+Acvz/fMa2ryz6tyfCndhuQMQZ8wiPxUT5VwCA2ME+scg5+K6LmtbfKysdUhmoDqo2L7O653f8fwIxonyDGF294A7mi1z8EIIIeZdewNsXukth4NwzebyxjMHknOKieZ0Z90YkwW+VcSmmXHL3cAavAL7o0DbXGIQQojFLJfMcfDrh0n1pok0h1n/2rX4wjOvW7mU9T3Vz/H7vJGy0dYI6+9ch+M7k8VUNQSpawvTf3gM54FOgk0BThwdpvkN64lsrJ7yuM1XNZHuT5PqS1N3US2NO+qpv6iW7FiOYHXAq2NaMHpsjEPf7MAZHKW2t5PWG1fiu3lbyWsuJXtSnPhFNwCZwQxdv+xh5cvOfE0e/PphRvcM0JKz9OUDBIOK+OVNp58PLfL+cpWanCql/nniKrxSQbcB/77wEYnpzCA/FROsyiU4EHLAwspMAqUUw4M51P90cmH3GE51iO7Na1i1KcYL3Xl27u9GWehviNOVaWdNuV+AEEKI+aUU3HkNDI5BNASh6UtaVbJKzDsl5yyvaS+ltNYuRRS7N8ac72r+CeByYB/wM+CvtNYtwG8AJZt5VGsdBT4PXAbkgA8YY743yXYvAb5fiAcgbYy5slRxCCFEsfqfGSTVmwa8xrGB54ZovKS+zFF5Mn1pRvcNEWwME980dUPjfBt8buj0cqIrSWYwQ7jhTMOkchRbb2yizzgMHQ7jCyhs3mVsd/+0DaT+sI91v3b25bwTcAjVBs/ZdmDPIDaTp848iS+dJscwvkQS3vDiErzCMyb2kB3/OJfKk+xNEawNUk2GurDlFW9p4cHqMCfG4OIG2FBbgZneDFRiLaiCbRMeu8BJ4PeRZHXBlTA/FRNsqnfxHxjAWsWa1XGUUvQ8O0zmwCDg9RLterSPVZtirFdZTgb9WGtpsjnq/XmkqoEQQiwDSkFdvNxRzFmF5p2Sc5bR+bKY8cOTFPBF4H8BXTM8z/8BThVR+5PCcT6O10A5aRHaWfoAMGyM2ai13gQ8oLXeaIwZnWTbPcaYxVlNWAixZPhCvgmPZ1X5pORyI1mOfOEF8ilvOHnr7auo2Xb+up6zOlfO4p9mgqFwU5ix4wkAfBEfgUlmSlaOomZDnLGID5vzGhX9TWEGnhvCZl1iK6OcfKwPay3NVzZN2gg6nVBdCJXP40unQYEv7EBvcXXSE0NZnvtFH5lEntU7qmnbXDXlttG2CLWbaxh8bohA3E/zFWd6h/pCDuGGMLWbvQmm6rbU0n5FI3fO6JVUtgqdTRRr7UvLHYM4S6nyUzGBL+jQUOPDWnCCPqy1VMcVfr8il7M4CmpqvO+tX9Eh7h/0kc5admwMEI0t3l5EQgghlp9KzDsl5yyvaRtIjTF3j3+stf488ANjzMGZnMQYY8Ytn8TrHjwf7gTeXP8WIIMAACAASURBVDjP/2fvvaMsO8s73efb4eRcdSpXV+fqqJbQUWglgpBAGAlskMEk22ADtsfGcx3wjO8wHt8Z3xl7lpdn8GJ58NjXNmPACIPBIBEUQAi11DpqSa3O3dVdOZ86Oe303T92qTpV5yp1dWs/a9VadXb49nfye37f+/7eo5lMJjt/rUeW6XoeHh4epyEdeVpp9oVIbUvQmGlQGakSXR0hsSl+3uPtyYor0LUv76ptY7K+II4C1E5UllwgdRzJ7uerjE8YRCIqd90VJRQ6WyDuvKcdLaxhVSxadiTPEpVfQ28J0PbhdVT3zqG3BSmVTfKPjgJQHa8R7gq592WyTv8vrr+kuaZvbkE6EjXfR7RWQAtp8Ka1F3Xu8RcKVOZcp5mB3QVaeoP4w4t//QohWPWuHrrf1oniU057LQkhWPv+1eRemXNFjBWSabyUrNCV/AWEEAFgPW724oCU8lruTHDNslTx6evNfAXTE8BnstnsX17l6SxKMB1E2eF+DmshDSEE6a0J+u9KMTdQIdLmZ/U9bQBEW/1slQ0cHFrjIRR9Zb9/PTw8PDw8TmUlx51ezHl1uN7qYFYBQ6fcHsY16l+MjZlMZg9gAl/IZrN/v9yT8/DwuL5pPHEC46cjiIiP0Ie2oXZcWMQUiqD73k4A6kcKzH3jBHpbkOhdHYgzvrQbPzxO86cjAPjf0kfgLauX/D68RqAjiBpQF0TS0JqlF2SnpkzGJ1zhsFKxOXq0wY4dobOOUzSF9tvSZ20/FcOWHJqDSDLC2ve4GZrjXzy8sL86ViPUEUQoAiNvIKU86/E9H0IR7hxufRAGp12/pa6LEyhPbb7VnKwy9uQELZvixM9jW3AuH1o9rNFxR9tFz/taYyV6QQEIIXTgT4B/A/hwsxabQojPA38opfS603icl0wmEwX+G/DY1Z7L+Wi/I83hvz2KtCUbProOcK1H+j+yFqtqoQa1BQ/oynCV+Eb3c8yu25hl17/Zw8PDw8PjWmAlxp1ezHl1WVaBNJPJfPtijstmsw9d5Hh7cEXQxWi/2HnheqL2ZrPZYiaTWQM8nslkxrLZ7OOLXPOTwCcBOjo6LuESHh4eKw1ZN3Ae3Y8sNlB2rkHZvHTvaTtXo/mTYfc6pSaNx08Q/siZFjLnxpyuM/vVAZjvxi5UQfSO0+dnPDd22v/LKZBqUZ1Vv7h+WT1INfX0qES9TLdA25H83T7JeMV97O5brXBntyDUFaJ4xC2DT26KL2RjprYnL0kcbcw2MCsW4Z4QiqbAmkv5uoE1b0pQK8xSHangm62Sf7nJ7PMzbPzYOhIbz58x/BpW3aJ4pIQe1YmtPXeJ/rXOBU0lrx7/DfgF4NPAM/Pb7gb+X0DBtfjx8Dgffw78GfDuqz2R8zGze5ZgW3Dh/5Yb3IUgIQRaWDvtszPQFuDlaUkDwbaogxbyLF89PDw8PK4dVmjc6cWcV5HlziB9N25G54+WYrBsNvum8+3PZDLDQB+uiS24YupTi4xTOuX/E5lM5l+AO4GzBNJsNvtF4IsAH/3oR1foe8jDw+NicL5/EOfVcQDssQLiM29BxIJLMrZQFYQQC9mCQrs0L1FrrrkgjgKYs2dXUSjJAPZsbeH/5cbX4ie1c/myFdNtOv39QUaGm8TiGhs3Xt59KjRZEEcBDuYkd3YLzNu7aIaC9IQc0je2YBQM8ocK5PcVKB4t0ftAN9G+82fG5g8VGXlsFCSEukKsfXj1QvbUxRJp8XHbw11MPjvN8DfmmHtmEnSNY7rCTZ/dfs5s0dewmzZH/mEAs2LimJJAq5/kpjjpTOsFz73WWMGlTh8CPi6lfPSUbQNCiBngf+MFqx7nIZPJPADEs9ns1zOZzHkF0qu9MG+UTyammBXL/U6TUP/6QayDsyhdEUIf3o4S0tm/roPdhRqO5ZBfE2THJdjLeHh4eHh4XG1WaNzpxZxXkQt1sf+fZ2zyAf8xk8kUT92YzWZ/6xxD/BnwUeAe3O7yf5fNZkcvc64XwyPAp4DsfJOmW3DV99PIZDKdwGQ2m5WZTCYF3I/bPMrDw+M6RpabJ2/YDtRMWCKBVEkE8L9jLcYzI4iYn8D9F+dP+Rr+1RG0lgBWroHQFMI7Ws46JvShbTSeHAQBgXvXACAdB+Pbh7AH5lBXJ/C9dwtCXRmNni6GrVuDbN16Zc9BzAdRn6BsuCJpVwQeH3J4ZhTwJ1kfE3wkoKCkA8z9Ux5pSxzDYfzJCfp/ecN5x87vyy8sL9fGazRzTYJtlyfkxjt0jJdGoeQgFIlSCDF3rEzdEQRjOunVZ9sLmFWLw397lImnJ9GjOo4lUXwKZsmkPt1gzc/1XdZcViqOWLGv3TgwsMj2ASDxOs/lDc8SxKdLPZ/zVTj1A/8VuO9ixrraC/Ph9RF+/JUpHCm5+z2tCCEwD8xgHpgBCfZYGWPXKIF71zBUESjpINKGOUdQMSHuf71n7OHh4eHhcXms0LjTizmvIhfKID2zPvRZzg4Azxm8ZbPZz2YymX8P/AzwceAPM5nMj4C/Ab6VzWaX2j/hz4C/y2QyxwAb+GQ2my0DZDKZPwbGs9nsXwHvA34tk8mYuI/B32ez2W8t8Vw8PDxWGMqda7GH58C0EZs7oH1pS5X9t/fgv73n8uYW0Gj/5GaM0Spayo+WPPtXppIKEnr/5tO22XsnsV6eAMB6dQqlN45+67msly+RmSIEfBBdGhF5KaiZku+dkJQNuKMTNrQo6Krgl7dBdgoiuuC2Tvj8Sye/mo7lJU1L4lPgtGaVF7Fq7E/5qQxXAVB8Cnr08gsvgkaVNWssxoYEug9Erc7hF0s48/uNWpLuLae/Juf2zmE1LBSfglEysOs28fmy/Pr09efVvhK9oOZ5Bfgt4DfO2P4Z4OXXfzpveK4oPl1qzlfhlMlk7gI6gd2ZTAagFXgwk8mkstnsH79OU7xovvd0kyEtBkiKuyx++73gCBgbNSiXHQIBwdo7IADIqsmuQzaOhM1tgojuhxXYEdjDw8PDw2MxVmjc6cWcV5ELdbF/65VeIJvN2sC3gW9nMpkO4GPAfwa+kMlk1maz2cqVXuOUa1WBh8+x73On/P+XwIrsHurh4bF8KGtbEf/2bVA3IBW+JB/K1wPFrxJYd2len9J0Tt9w5u3L5RvPwd5BUBV4307YskSi6xlYDZvGTINAi9/tDH8BHj0ueXXaoXC4RLbY5FMdDTa9bxWpoMr9q08+n11hSWFeP2wJCnyq66HX+45uxp+aRNEE3W/vvGCzps6721F9CkbZpPXGFFrwCpxpOpN0rdPx+0yaTVDu76F0yjdgYbJxlkCq+lUUVSG5NUljpoE/6UeLuGX18f6l94W92qzQQBXg94FHhRBvB56b33Y70AU8cNVm9QZlKeLT14tsNvsMsOBVkslk/s7dvDK72E8UHNyEGsFMWWLbNjPRGGPpFIZdwRf1o6Vb2QpMjpqkbAcLgT3jUK75SCx9Pz8PDw8PD49lYYXGnV7MeRW5rF96mUxGAwKXIW6GcdOCI0CFFeuL6+Hhcb0iQj4I+a72NM6N48ATe2EkBxu74K75jNF6E77zIuQrcNsGV7icKaFt6MLuS2APFVB7Ymg3d135HApVVxwF14rgp4eWRSA1yybHvnIcs2KhBlVCHUFq43WC7QH63t27qL9myYBGzsAoGIAgP9kg98rcWV3u37tBkA6BYcPtXWJBBE30x0n0x5G2Q+6R4+QPF/H1hGn90HrURcRPRVcQO9K8PGITrgrusSX6JXqQLhAPI37l7bQeGoP2BGZfO8PfmqRZtwFIdp1dup/akaIx26Q6UaPzrnY67mqjdLyMUAWxNddfsya5QrPPpJRPCyE24q7mb5rf/AjwBSnl+NWbmcepXEF8+oZDSsnjJySDRcn6pOCtq90yw86+AMPH3NWl9i4/qqpiYvP1/o0U1ikEFMmnFfez2dYUUooNSFRV4HgepB4eHh4e1xArMe70Ys6ry4U8SO8FWrLZ7NdO2fYHwB8BWiaTeRz4YDabLZxnjCDw88AngAzwTeAXs9nsE1c+fQ+P6xPHlsy8MEtzrklya+KCjWQ8Xj/MV6dx5uro29tQUstQev7igCtIAgzPQFvcFUof3wv7h93tX/geJEIQ9COeP0rg19+JDPkR2hI17Ano4NPAsNzbS+TTeibFYyXMinuN2niN4tESkZ4wleEqM3tydNxxdoOoO7oEx064/68XTWI4izZN8qmCt646d9BTO1Cgfsj96jJGKlR2TxN/89nismFJ/v5Fk5rprudVDclDW/RLvq8LpOPuH6ADO97VTm64TiCq0dLrPs5W3aJwsIga0kj0x+i5//R5xdfHmKpKDk9IVsWgPbzygrvLxVmBgeprzAelf3i15+GxNPHp1SKbzf7S1Z4DwMtTkp+OuhUHY2VJWxi2phV+6d908+PHC9iW5M33uZ9VJb8GEQe17KAGFfJh1wLmXbf4+csnJIYpecsNPpLBlfv+9fDw8PDwOJOVGnd6MefV40IZpH8APPbajUwmcyvwJ7geogeB38N94n5vsZMzmcxf44qjR+fPeWglBqseHiuNmd0zTO2aAVwRaePH1uNPrOCsxxWMXTZoDlVQkz7UqA81ql92aX1z1yiN77ue2cbuccK/kUEJXYFYthjl030lrZkK40dHMZ7Kk3YgngDyVQj7ISChWIXJPGJjt3uCaYF+ccUB5aEKs3ty6IUSnX0K6o5V0BJ1fUc/eBf85KB7nQfOaa93RfhOeU1LB7RTMkalfbLAwLEcpp+fxSgZ9GxP8u/uD3JcK6INNwh3xWjZkbrka5/5EhDnyHyqGCyIowDTlaUtfAhEtNPK6h1bMvC1QZo5t6FYY6aVzrvbTztnvCL521clliPRgE/sUOiMrMwA71JZaaVOQohzNd45DSnl8HLPxeM0rig+fSPStCSKYCEDvmKcvv+124GAwjveffpnaswn6OzU6ehwLUmSIXeM2ZJDd5uC5YDRcLAdWKp1Og8PDw8Pj+VmJcWdXsy5MriYJk1/cMrth4Fns9nsrwJkMpkRXD/RcwWgnwCGgQlcv4QH5g3qTyObzT50adP28Li+aeROdluXlsQoGp5AehlYpSZTf3UQY7JG/dU8wa1Jone00/Lw2gWRVBoWzNUgFUL4zv+RaA+eXN9xqgbObA1llZthYzVspp6dxjEc0re0EmhZvJVveahC/kABf8pP2y2tCEVglEys0SK67aBtWYV4+QSyWMNBY3Rfk2LVQMSSDO8rsSlsoWfWwVwZdh91S+B7WiEVha8+A7Ml6O/GevA2hE9D9S3endGqWwx9ewRneBYOjyNTkt7Nh+E3HoBIENZ2uH/LSGxNlO63d1EerNB5T5DaZJ3i0RKh9iDpm1sWjpt6dpqZbA6A0rEy/b+8nh3v6sTte3J5BLckCd/YSv1QAV9PmMhtZ2erAiQCsDqpMJh3M61u7Lr4X/+O6eA0bLSoK6IXDhdpzjWJb4yf8/Vhls0FcRSgMlSBMwTS4wWwDJvoT0fQZ6q8sjdM28f7aOYN7IZNZFX4nILvSkeuMF9gYJDz2wGJ+f2eLPT6cqXx6RuKZ0YlTwxLVAHv2wibWwQ3tgv2TAryDUk6JNje5r73js85/M9dBrYDn75VZ2u7Sl9E0r1rlNHhBvGkxvbf7gH8jJccwvNfm6WmpGpAfOX09PPw8PDw8DgvKyzuHMSLOa86FxJIE8D0KbfvBB495fYLQPd5zv8HPJ9RD49LJrk1QWmgjLQlgXSAUGfoak/pmkI6kqld04x8eQCOFtANGw2JOVOnfiCPMV7D3x1GFuvYf7sLOVNGhH2on3kbIrK4cAWgrU9hHnaFOiXmR20LL+wb+d4Y5eNlACrDFTb9ysazRKpmwWDwW8NIa/5jUbqZi2OPHMM6Mke620frtgThT7+Dxj/vxzhapPqTMaxwCL2/BXnbRqyf60Ffm4QvPw25ipvxOVOEr/3UFUeBscfHyL36IkpXgr4He8+yaLAnKxhzTRzTcf1GAcMUbvOqyQKsf/1+4bbckKTlhuTC7cWaJjXmTqY6OaaDWTbRI1eWuSsUQeq9qy94nKIIPnKTzom8Q9gn6IotLjifSXO8xtT/OYZTswhtSaBsjDP+5CQAs3vm2PiL6xa9D3pURw0oGEUT1a8SWRU+65ieKASGS+gzVQSCUKHGwCODNOa72kfXRln9nt4V14TsYlhJK/nz3HLK/wL4MfAhYPTqTMdjniuNT98wmLbkq/ssjk1ZKAKkrfMf7tKI+gW/kVEoNiAeAG3+++pzP2wyWnQXhP7vH0q++ZEgR5/PExgrsV4FSgav/mCGnR/oYUu7ymTZPbY3oRA999enh4eHh4fHimOFxZ1ezLkCuJBAOgGsA0YymYwfuAn4D6fsjwLNxU6EleOz5OFxrRFbE2Xjx9ZhFE1CXaFzZgF6LM7I98Y48c0h7JESqaOTiKZEtoRR+xMgBErQXXhz9o4hj03BgRGklEjVQvzBg+cc13dLFyLux8k30De3gipwyk2UqJ/m3MmPQrNiYRvOQsm4dFxB1Cgarjhq26CqNHNNigMl7Oka0jDJ7ckTHxii3hnCPFEBRSHZ5mN8qAr9LcQ3JwmsTbr14Ws74OjEycmFA0CRZhNyMwK6FJymzfRzM6cJpI0nT9B8ehgkxFU/xWQEMVOkNS0h5IeOxBI+E5fOYqJeamuC8mAZaUlCnSEC6bObGS0nmirY0Lr4Yu1z45LjRcmauGBn18m5F5+ZxKnN+6seKGA17IV9dtOmMdtcVCCd3jVNI2dQn67T9dYOOs7IHgVYHRf8zAaFgWOCpB8SfkFlqII232SqfLyMVbPRw5fVh/Gq8nqY5QshFCmlczHHSilfPONcB3hVSnl8WSbncbFcUXz6RkJKyeEJC3N+Ye7QuMVr4b+mCFrOWH+dqTjI4TLCkcz1xXAcB1U7/X2pqm5Mcs86je64oGbCpjYF5RrNXPfw8PDweGOy3HGnF3Nee1zo19NjwJ/OG98/BFSBn5yy/wbg2DLNzcPjDY0/6cef9NIxLofisRJCQPvQMOFqAcIh7FSUYF+Y+AN96ClXYBOxAAzPgpRu9/jDo5Aru1mZZyAdifH8GM5cHV9ag//1fYznRrHW9qLtXEvL9jYmfuImNMU3xhbE0ebLkxh/vQt1agb/XasJHGzQmKoj4kES77qL2mQdM+rD2V+GepPKVJXaf38Gpa+D0PoY4ZjKxvva8H9wA774Kf6pt653M0dHcrCpG+7ajPNPu7BfmUakI4SnJgi9MIreG4d7W6A1BoDx3Jh7voCEMGn7nQzq2Cp89Sps7nHL61cYalB1rSbKJm07wyja2QsGjiNf9x/nh3KS751wY54jc5KYT2FrqzsHJXjK16sQxNbFqM3OAqCFNfwpH2NPTNDINUhuTZLamsCxJSM/GKN4uIR0JJM/mWbNz/Yteu0NG0OI3QrNkkl8YwppS0oD5YXx1cC1WX3jLNNTKIS4Bfj3wK3AESHEn0kpH73AaR4rFy8+vUgUIdiSgmN5UBXY1nr+4/v3j1M86lYjBApFFGUTa29JMLa/zMTRKvE2Pze886QlybpzLB55eHh4eHisdJYj7vRizmubCwmknwO+ATwOVHC7z59q6/5x4IfLNDcPD4/LRO4dgSNT0JtE3Lbuak9ngcZACTPXILgpgRZbPk/VUEcQ9fAk4XoZTZcEoyZOI4+ay6FMxJGdOqgK4oZulJu6kS8MoMzmUYbr8JWfwCfvA5+O89IIzpFplL4URl3Q/PEQSInYtQc5U0bm69iHc9TxkdrZS+TDa7ENh3CPm5LjWDbGHz2G9soxUEAeH2HdxiS19V34fDX8uSn0h9YwEdexRkcIjluEjALaWB2pVlBuvgW9WsWXy6E8WoX37wTffNahqsJDty7cZ6dpUSxGsaMarYU8+vFB1JYgsVYVntoHD98BgJIKYk9W3CFaggTbg9DueoIfO9ZgYn+ZVEpjy5bAVS/RLp0oM/b4BLMv5Qi0BvAnfMw8P0vrjS0L2ZG2I3nkFZPD0w4dMcFH3uQj7H995p0/Iz8tf0p/reTbu3BqFtZck+itaaI3txLsDtPMNwnEdA79yV4KJyqE1kWpjtUItgUItPppzDRPZhwXDJp5g0Dq9IUSq2Fz/JFBHEuihzRCHUFS25JMPT+D3bBJZ1pR1Gszk+tyV/KFEDHgZuA2IAx8U0q5Z35fAvjvuFmHnwTuB/5VCPE+KeW/LMW8PV53vPj0ItFUwYdu1PjqKxZ+Dd6//WTmenmoQnW0SqQvQqTHtfPoGJwlWjWQQGikgW3bqKrKmz+++GKNh4eHh4fHtcrlxJ1ezHl9c16BNJvNzgL3ZDKZOFDJZrP2GYc8jBuYenh4rBDk4CzyG/MZ+vtGIehD3NB7dScFVPbMkv/2IADlZyZp/7UtqMHlKQFe/dAq8oU51EIX+kwZe7iE6PSjRP1YX34B60d+REhHeftm1N9/gOav/yPNKQM1kcA/U0QcGccJhrC/tRcA++AkTnC+TF1KnHwNWTUxpGC8Jhk42MQ4Cu+7+wxRcWAWJVd2M1RtoG6jGibR2Px+n0YwHWDt+1fDOofCf3kKMV2HZBhFVwhXJlGkDQ5wZBx2HYE3b10Y3iiZVMeqBNuCKKUGdq7uDpvyESnp+De7DaSYz6x0qgb+e9dQe+QAQlcJfmDLwlhTUyZ799YAmJkxCQYV1q5d3gxm6UgOPzfHSM5iyw0xuvtOz14d/f44Vs21KygNlEnf3IJQBOIU8e/glMOhaferaaIkeW7I4t6NV+ZPerFsaYFnxwRlQxLxidMys9SgRtsH1i7cLjUko4Egnf0hCv94jMZUA6duUz1QQL+rHatqIdIBOu5uZ/zJCVAE8fVRtODZ2VnNuSZW1S3fb1qSwwcqbN3aQtebl7ep1uvBYmb5QohP4gaZr/FFKeUXT9l/H/DHQBtwGLCAXxFC/L6U8kvAh4HVwGellM8B3xVCtAKfEUIclFIevtRpXuLxHkuMF59eGs9NCZ6fFagK3D4HG9Iwd6LCv35xkFIDEsFZHvr11cR7wiRmSkSKpntiQ0NRPIsfDw8PD4/rkzPjTi/m9LgodSKbzRbPsX1uaafj4fHGwSk3aXznKE6pif+uVehb00sz8Gz59Nsz5cWPe51pHD35MWKXDMypOurqs0vZrxQpJUdfKJDTknQn46RXRVF7EtCVBilRxnLQ241s2hhfeQm7CJaWhFgTKwfKpIEvFoLJ6mnjqukg1kgDqSg4W9YgDg5SmG4y1tLDwE1rKdZ1bp0wibw4gVOziN7Zjh7U0W7sxCmUEPUGytZ2eNt6aJhY6RYqc37UXVOoSR+FpyqwfRthy0b3O/i6QiipkFvyv/DAnbSwMYoGR//xOHbDRmiC1fd3InQVadrIcBjrln7KLw9hCB3tjk4SL4xT/+5RzOwEpIKo3VGM58cJvms9APX66fY45ZEKVsRGa1u+BmF7n57lf79oYUnBdweKfPbDGp3pk+KmtN2YILYmQmWoii+m035n24J9AcCZ1fbavHjamG0w/tQk0pF03tO+LI3O4n7Bb9wE0zVBOghBffFV6HzN4YvPGdRNiU8V3F+DSDpAo2DgOJJQZ3Ah63jNz67Cn/BhVi3SmZYFX9FTCaT86FGdUt5gz7hDIRnk6d0WH3+TRkf02swcfY3FSp3mA9Mvnr1ngdc6lj8rpcwDCCG+BnwC+BKwGTgK7DnlnK/hlj/tBA4LIYSU8qwgVAjx7TM2BYC/FkLUzpjjQ+e9Yx7LghefXpiGafOF3RYN0315/+kzJu/tV3lpX419po85n06rYbL6QJ27e8Jst0uMSBUJdDZrC43zGqbDsVmH3oRCPOiJph4eHh4e1z5nxp1ezOlx7XVw8PC4Tmh8b2ChI3r9G4dQ++IokVPKzh0HHAnaJfp7beyA6GEoN8Cvw7aV0cjXvypC/WAeACWkoS9To53ccJ3p41VQNQ6FuhmuN0mmDFpeGEJ1QOtM8SOzjZnhBr2yzrZ/OYbq6Og9LVBu4Ny0HlalEYkI8tEDOLM11K4ovoe2oBkSJ99ABG6g+LmnmAqblIIRrLUpBALniWGqAwUAmifKdP5f29E++CbkpjaEX0V5z3ZIx5GWw/Tn92EPuJ6lzaEK/r4IoFLdsoWuLTbEQ/C2bfDtLBweg84k3L7RbfD0zeexdg0RntMobd2EtBQq0watH9tC85UZ1JYAM0WHgtbpPij7qlAYwWfZNCdqmPsLqH01rJqzIJB2dekcPapSLtuIgTyJl6YpPCUJ3ddH6M7leQ0dnLCx5ttHNi04PG6dJpD23NfFyA/G8Pl87Pi9NcQ3xM4ao79NIdOrcXDKpjuucHuf+34ZfmyMxoxb8z747RG2fKr/nPMYLEqeGZOEdXjHakHoHELnYgQ0waqzp3UaR2cd6vPihGFLZm9sZ2O1Qee2BLE722i7vxsxn+WrBTVWvavnvOOpAZX1H1zDs88UmOtSMbuiYMOhWYeO6LXtB3iZy+SHpZSHzthWAV5Lna8DcSmlIYRQpZQ2cADXt3L7/DHiHJfPnXH7/1zeFD08rg6mBdWGQ8VyX+TRmvsyn0yE2RewQUrG9AC3J9wS++jObjqeHnMb+WXaURSFYt3h01+vM1FyCPsEf/6eAP1t3k8IDw8PD49rm8uIO72Y8zrHi248PK4Ssmae/N92kE0LXhNIBybhaz8Fw4J7b4C7Nl/0uCIWhF97K0wUoS2KiF5G0x3LvnRh9gJEd7ajRHSsXIPQthRqeJnKoOe1LTdLtYYMQ3PvJDMFC12FA8Fu5qwQMiA4kmwjLYq0D02i39CJEvPj+8AOAMwTJRpWAKmCGkugJUOoqoLaFaX59BDahhY29Nm07jrI5m8ME9vZhy/azWsmeM5EidpXD6DaJpotQVHdP8Aum9jFk3Z5dqEJ853mZTIKH9lx8v78wt2uWP5amePuo7BvGkS9JAAAIABJREFUGF3YBCdmMVJJGt2dBNsD6L1R9N75rNzHRk9/WIIaNGzMmlwYq5EzkY5EKAKfT+GeOyPMHCkjd08QDMwfs3ty2QTS9RtDfGesgY3bSbmv53Rf2vjGGPGN51cfhRC8e4vOu7ec/nqy5jvIA9h1e+F+nknDknz5oMSYz1a1HHi4f2mzMNsiAoFAzsdCfZsirLl7O9KRizacuhj0qE7f7S386GV7IcRqj1zb2aNweV5Qr63CCyHSwAeBO3ED1U/PHzIErBJCKLiGFQCTQAFIz4+xaIdRKeUvX/KEPDxWEEGfQrfP5kRTIID++Szzrt4AoQ2C6myTaHuAtk738/d4XxfDQQMhJW09XdwM/OCIxUTJfYtUDck3XjX5d/d6PyE8PDw8PK5tLjXu9GLO6x8vuvHwWEKkZSOPzyLCfkR34rzH+u9ZhT1WRho2vjd1oracUgL8w1egOS+gPrEXMusgcPFNjUTID+vaLnzgmZRq8KUfu93Rt/S6TYGW0H8svD21ZGOdi5beIO3rwkwdsAlENKJBC8dyKDY1fKqkagjySoh4bxgnV8c4Poto96GkQ4Q/cSNKUMexHMyXJpATBbBs7HE/zkwNtcMVMdXeGEIIfLkcvbKCvr4DxShR88fI5R1wHPTpItZhC/PZE/hWhfGtiWJ/dz/ax25DjfvwdYVpjFSoDFWgM0KzaBJsC5B6cJFGGKc+B5ZrtadHNBKb4oh1EfS3dpPoj592SvvONmoTdYyiQcsNKVLb+mg8NuA2pZIS4n6UdakF0dA2HIb+eZD6TJ3miQYd3T5CMQ01tTyZvgCNtgihdT7yVUnvao3VqaV7rbXvbGP8yQmkI2nfmV5UHAWoWyyIowDF5qKHXRGrUyo/fyMcnXHoSylsbneF8nPN6VxIR2JbEs3nPk6rkwo/vw2O5iR9CcHm9LVf9rqYB+klsBn4Fdzu5XXgy0KI9wMvAa3AOinlUQApZVkIEQWmhRC6lNI816AeHtcyQsDdazX80+BT4I617udEf8CmL1+ibknCuSYbgilAZd9Pi5hxd6Ft7uUqtm271h2ORBg2UldoDZ/8rDFyDZyGjb8rdNUb+3l4eHh4eFwKVxB3ejHndYonkHp4LBFycg77L55Clg1Ix1DftRXl1tXnPF5bkyT6uzuRDQsldkYzHP2U7E1VWWiys+z89JArjgIcGIFja2Bj14XPqzbgpRMQ9MFNa5ZUVL1UhBD0bQqT0BzqHSqNyRpKoYi9J4eUCqtCUA8JgjelaXtugL61AXwdQZzZGlauzvBPRqhN1PFlh2gfm0ACxmQJcWg14XwJtb8DbU2S0Ee2Yf9YRw9UUeJ+pJTI4SoRv4YzV0ckNIR0PVGd+WzG2mCJwmOjhLtCpH9xIwP/+SXqE3VEK1g3pOj99Cb8yQs0Rrp5HRwchZFZ/Df2EH9gO9MvF6iN12m/I43qU3AmK6gBnbbb0ggNEhvjCCEI/9IOfO/uZ/jvj1KbM0nfc7KUuzZeozHTQCDwbW+l3jSI7Ujx0sYeykccbusWtIWX9nW4d0rS1aLR1QJ1B6rmySTqK6XlhiTx9VGkI9Ej585WTvhha6tg/6xEEYKdXcvzXtN1BUMVFExwpHutS6E002T/E7OYTZuuTVHW35YEYHNaYfMS2RevBBbzIL1YpJRPAzsAhBAp4MvAf8UNYKeAnwX+9JRTNgE/8AJVj+sZIcAK6JjCRgqB9Lvfz+pQkYftOWZVjbRloYzo0NqCNE5mpWNYCCHY2aXwc9VZXpmR9IbhF9a6cUHppRwz3x5BSohsSdD+cJ8nknp4eHh4XDNcbtzpxZzXL55A6uFxhUjbwTw2h/K3P0Q+P+xu7O/GeWXsvAIpgPCpCN8ipewPZuCbz0PDhPt2gG9xgceca9AcKKF3hvD3RNzGT/961O1Wfvcq9EIODo1Bbyu8bbv7S+l8nNXx5iKETtuG/+9JmC25t6eL8MCbLnzeMmGWTY599QSO4VYv9NzXRfLjG5j410EmXyoQCGr8zPYgve9OUtdjGHvcZkzCp5IfrFGbcDvBN22HXDjC3JyKNVUi8pvfgHSIng9sJfXJN6O0hDBbU1itaTS/Sbnho3zCJJBW0TsjWENF8KmUWlIUyg68ZFCuAz95lWBHEPlrm2keL4EisKdrVH9Y53DA9Z+MrY9hlk0cy6E6XCXQFiDS4/rD4dfhE28Hy0aqCsf/5ihmyf2uNeYatFWKWENF8odKVNa24bRGqI3W6b7X9SMt1hzGWuPQCoXDdQJ9DRIdAXwJH0IVSFuihHQSb+/m6VQLeyYdwOFQTvCZWxV86tL9+G2PCKbn/fBifsEi/YiuCC104QGFELx/I9zT414/5l/6H/e5muQr+x3seT92IRTuWXVp1xl+uYTZdLOHxw+V6doUIRRfJpuKq8jllNifybzn05wQYhfwM7i+T38F/KYQogn8DfAZwAB+csUX9PBYwdgO7JuFWUNBCHhlxt3ui+kYRROt1sQMK+hR9/NkzY4oQ3sKSKBrUxRFUcgdK7DHDnAo7mdWsRjeW2TL24MUd8/yWpuJyoECreUutNgSrXJ5eHh4eHgsM1cad3ox5/WHJ5B6eFwB0nKY+YcjNF+aQOxrkrA0/JoFhSqi/fQO7c7LozhHplFWJVFuX3P+gdsS8Kl3nPcQq9Bk+ouHcBoWCEHrh9cjs6OYR1x/58YXd6EFywhdgcFpiAXhlg3nv+7dW1yBc7IAN/TB2o6zDmnuGsXYPY6SChD8uU0otnVSHAX3WqciJfbj+7D3jqHctArtbVuQhRr2d/ZBzUB5ywaUje3nn9clUJ9pLIijANWxGqntSbp+dh2h3hly3zgBT48w9YMjhLcmUFcnUPwqvtu7qYw0aeab6DEdJR2jWjcw/KAZVUzTwiqbHH1ikNs+fBu1L+3DztWACPm6j6I/RDA3QGOmSWpHkuB7+1H6Wyj4xkEXjD02RvN4GcWvEMobJH4ySbA9SDNvYMwYEPMhHcnQv47gi+k0ZpsUj5VIbIoTmpmmp8ch9KZu149WCNBUHMNZEEcBmkfmaJ4YRyIw5hpoeoGGqpP75gmiAUF0ZzvVOff4etmiljcZfKnIjnf68Sd89D3US35/gUCLn/QtrUztPeVxNCUVA1KXYWl7Lh7cIEgGFOom3N4tUF+vTOkzEELQHl6+8fMNFsRRgJnaeQ4+B8opjaOEEChLKFSvJK4kg1QI4ZNSGlJKWwixGtcb6qCUsiaE+B+4eXGfAP4LUAI+K6V87oon7eGxgjFtyXRFUjJBIBmfLxKpxUJMmSrOdJ3yqjgbo0HiwKqHV/F0JI60Jbfc71q3PFXU2We7ditjjs7XcwqfA/Skn+b8oqIaVFEC13aTOA8PDw+PNxaXG3d6Mef1iyeQenhcJlbRIP/oMOXd0+hRH2gajWiKYLgG925GeWDrwrHOiVnsf3kFAPvABIR8KDdcXOMbq25hFE0CLX4U/WRGZ3O44oqjAFLSOFrEVz3Z+IdqA3QHXjunVF/YZeydwtwzidIaIvCOtQhdRdoOIuCDD7/5nHOxpyo0vj/g3qd8neaTgwQeWIdsiaHk5kXSda6oah6YwcnVUSolGn/xJE7TRjx6iEAggHJ8FnnMTWOxH3kJ8bv3IvxLkw0XbA+iBlXsupttF1kdwTEdpl+YJfedIQK2xNgzjllpoPskeluI4O/tpDxYYfbFcRqzTWpTdVY/tBHjSIKx54vkGhrhZpUWtYIVCeGoKnauhnQkIwNNZibLyE6Tlu09+KeKsLWd4IMbsE2JCE7j1C0aU3WE7SBNgTVVJvjsQXyrk6jbE+SDKmxNIYSgOlHDMR3MiolZNrEHpokNHsCaCkI+BwEdRwsgZyso27tJbIpTOOT+4o3NFrAGCiAliqFgdalYL04TSPgo/nAUaTm0bG/leLbAzPEaQoG5kToThyt0bYoSW+P+vcaONsHYwRwMTtMXsEhsWgPB+KKP++XgUwVvW319Cn2nsioObSE3W1YVghvbL/0+r80ksJoOzapN77Yogcj1+fW9qGv9RSCE6AH+rRBiGLgB2AbsA/4AQEpZBP4fIcQjQFVKObIU8/XwWOnoqsDB9R8VCJz5SpKZQ0XkbAPh15DTVWaPlmlvT/G9QYdq00Y4ku+dcHjnjZJgix9/UmLVbFSfQrjLtYJJP9iDElRxahaJu9pQFquI8fDw8PDwWKFcTtzpxZzXN9fnLywPj2XC3nUcOTSH7Eoy8tVxqrunMI6XCLZpBDr8+Le3oXxyG2LT6b6dzuFpnOkqIhlwxchc9eTOehPKdWiNneXdWZ9ucPyRQeymTSCls669hpovwQ19+LraEZqCtNyPdv+qCL4NEex/OoA0bbT7tiCmhmGmiOP3YaVa0KomsmbQ+OZht1HPYAF8CkqlgbV3EqUlhP+jN6IkFk8TlIZ92m1zvMrcn7+KMxMjNN0g0J/E378a+/kxGo8dcx+zV8dQatZ8+bZDc88EQZ88ZRAbDNstHb9CjMka0/90gkCugbouRstbOon2RTjy1QGO/fMo9kwNn+PQkauh2jbqYIVk1Ae2pHCwCBLiG2I05xpMf3Ev5YEy04kk9WSamhonl1KJ3raaH32/RGc8zuCLs1RGLWRrmETTplJx8N/ZR+z9ruis6dC+M83QX7yM0EEJqIiqgd9uUDpikI5Kuj++lpZf28bQd0awDZvklgSVgRKyboMAtV6nNGuDbhDqsOHpoxSnJXm/j+ndeZ654ybk1hYe6rYIf30WZ1sr9liFeDJA451rUX44SqjDfT7NqTrxN/vYcEcKOZjHp4GjsZBVeia3Rht0H91FRaqsM6oo3y/AR99yxc/TGw2fKviVmxRGSpAMQCp46QJpIKJxwzsuo/HaNYZz+f6FOWA18CZgAPgfwJNSyslTD5JSHrqS+Xl4XGs4ErYn4ZVhC02Fm9Ju6B+TFkq+il230MI6UeEuuNZ/NEr/gRkEkJtKwc/2864NCk8fU3lxRLAqpfDxm90x1KBG24O9V+uueXh4eHh4XBGXGXd6Med1jCeQenhcBNKRbon89w8CMPeVY5RfNXBMUAJgTDcIlgtYzTpzikbqTztPdgc/Movx9AhyYA5FgHZnH8o21w+SsZzbNb5hQF+a2n23UZtuEu4NE0wHmNuXx573HVSyxzCZQW31w9EJ9E/dT/qX+2kcLuDrChPclAAg+ju3Iw3bbfxk9mMP5ih+cxDnW0OI4BiRt/e64ug89rEcznQZAGd4FvPb+/B/7JazH4NSHWU2j74qgjlcQYn6qVsaTq0JA3PU5poobQ72V/aj9sZOntgWx5meQcXGCQTQbupDSevYj7wEpo1y+xpEdGk6pc/9YAwr33RLtQcrBJM+KiNVXv38YcyyCQGVSRM0xU+qVqEyWiP9qR0ITcHf4qc+Xac8WCG85yDxYg5EEM0fJp4OEritD0tTiSbC1OeaPH5AoqpRxKoIZTTW1GtEAwqx9VGMoonddJAVA+ObA4RenmaNFEykI9jCoE02EEJQOl4h6oDeHab9fX2Mf24X4y8XaBoK0Y1xUt1hclOSkkwyN2FTN8rkO+N8Ve+iGvRxuCXJjTMGPsPiy3urfOxYjVhSYS4So+EPEUcQ3Zqk8eMRZKlJJK0iHUliaJrOI6NYhoM1nif9wM6Tj+G+PPWpBrH1UaIJQbc8mXnMKa8bj0vDpwrWJa/2LFY+8vLN8uvA+5Z0Mh4e1wG6AmrZQNFUEOArGoBOwqlyTKgMRyKskTXut13vj83HpzFnKoAkLU2gH6du0/fCKJU5SXdQotzRA6Fl9CXx8PDw8PB4HbicuNOLOa9vPIHUw+MCWEMFal/ZD4fG0aWBGQlTm7XBcrAboKqSmFXFX2kgRyTNJ09g7tmEL+NmkVr7p5A+jVKinfqhAs6zBpHOMdIfWIu6+6grjgLV/TMcP7EfmYggNMH6D67FF9NRh+fQT+TQ5mYRO+azLKWEQhX/piR6W/C00nsR0BCB+be2rmHkHZyaK7IeaOpUXq2wLhGivVBD+DV8N3dhPnYY5cQo6tA46vgwdPvh3hsWxpTlOvyvJxHVJkFFEHj4NsSWToyvn4CZJjQt3HRHgWxYqKviWEfnkPka6kQe+rsozwnk1h46t6RRkn7E794Lhr1k4uiiCMHo4+NICTaCE74wB3uTNMZm2Gwr+CqQGjewd02R2JLEsSWReoGW+jTStEjKOqvmRhlOBjEGS0Q2xEFKat8dIDzTxPKrmAgaHTEIaZROlBn57igD/3SCSHcYZbSEuncWEfChFBqsdppM9Ldi5CRKpUKzO8XL3d18/2mD/E8n2bC3Qs9UHQcFsxAg0BKgMlzHMKL4dYeSP8pcqoVSRcMxJQ0UJgfm6H/+ONGGSaPfR7miINqDKB0RKiM1lMkC/tFpFE0w8/gw4zkHfaZM2/owRt1Gt5uEv/IsZt2g2tXGaN4tncy9OkfXQ33E796GvusghANw/43L91x5eADOEjRp8vDwOIlhSUxL0qXbKAJKTXf7D8tBvr+uF8V0OOhLs7mk8SDQXqxSKbpiaWC+2uNHT87x5AkbaUmmVMFXvjbDp3/HE0g9PDw8PK5tvLjT40w8gdTD4wI0Hz+BbFjQFsd89ihWG4iwj8h9XdQOFghgEBuawFeahYpKM96LrJ0sWVY6oxhPj1IbN2iWwIk4OE+MokY0AtMWxkGTUJtCxdSQcfctKS1JdbRKam0Ys1zGDCgE1qaxZ3PQCrQnsNMpZr6wH3O6TmBjgtYPrEWoZ3edV1vc8uqDSpADwzk27H+VKV0h5g8R7E5h5xpoO1dhfWs3tiXROzSUXYcRpwikDM1Cdf5XlSMRg9OIrV3E39GDXbOw6u34CiVUx0Df3kvg7lWobWGMzz+N2h+l9uIsflWjoWrMffMEbR/f5HqOLpHv6Guk3tnD+H96AXOoRPz2NGpAQdUVYr0hpvaViBfrrEuFELpCPhKiI1+h+INRzOMl6s9OUThUJFaoITUV1W+j2oJWrYncGiW5OkDdaJKvB5GFJqmwoKoKanWHzhtTdA5NMTdkMFfLYxRNFE0hoiqYMzWkrmCEAgQ2d3Dj72zn+NcHyekaP2zvYPdPQQ/D+nKNOeGnU62AJSmP1WgWbRrFJrYtsQyBb7pOmzqNnujE0XQ2demknhqi40SZVLPOxLQg+vZ1hDtC7uvIkdjH5wgGBeWGYHawTmCNQaMBumURFA7m/jFKhTDR1RHsp45gWq2UZkyqQmPQ9BNc287Nv7qBVLt/SZ8rD4/FuJImTR4eHmejawK/LrBnLWwBkTa3y3yuLYkMFLE0G6GrzLW5Ke5+n8CJuF6iPr9ASsnUsQq26bgtJxzJzOBldJrz8PDw8PBYYXhxp8eZXFcCaSaT+Qjw+8AW4Lez2exfnufYXwU+CwjgMeC3stns5faH8LiemW86YI1VsJUQWjqKkgii9kYJ7UiT/uBqrA/9FdaRIJg24XgT/U2dC6drt/Xim2kgRo4hTQ10d7z6vjwN4QM1TmHUZOr2dRTHbBLSIhzTCEQUpGkT7g1TbzgUGxLLVPGFNMS2dZT3zNEYKGJXLRzDZmJLipH2JOkgrE0I7IqJMVZFbw8Sec96Zl5psHb/PpSwTmCoim1UoLcFY/cY/h4/mlWBhol9QsHe2AqzdRqHCmhtQYSlY+7PoygQWBdDdLnl/FrMR9tH1sFUC843dsPwDMqRKoy0o/e3Qk8YWTVwDBunbMD+CSy5fD6KolAjmZaQjoLVwNg1Sve9ncw9PYXRHqDi89NRrmBVTexiA6dcR331INoJm3KoE0eJkzPDJJMxkl0KTsXh0Hia+qMjyJtbSN3WxuY3xzgymmZgdx7LgtSOFFsSJrPHJc2mTT3qRxUmVtWi0SgjZQMx56DHVMa2ddLZGiHzRzfxj1+aYGbKxrLq5Is6rd0pWsPjBHw2hqKht/hpWBLHlqgBFdWnkPYZtCd07rPzDGpxNm8SaH9zglrdxm7Y1G0fHSGN6NoIlaEqkb4wyWqS3HcKFAsWdb8ffywAiQC5gTz2ZJ1UIkpwqobqU1GaDvkjJQxHYDYlypE5rFUxBo82PIHU43XBW8n38FhapAM3+y2clIKuwA7dBIL8zDY/PxqJM1dxSEcV7ut3FyyTWxIomkBKiPSGURSFbd0aP3i2Sl7XCdo2N7Ve3fvk4eHh4eGxFHhxp8eZXFcCKfAy8EHmO4idi0wmswb4j8BNuCa7jwEfAf5huSfoce0RfHMv5vefx35xGKe7Fbt9NXGhEPrNrWgJP0IRKDt7UNtDyKkS6uY0SvBkJ1chBOEHN5Lyh5j50hGsgkFgQwL/qjDGaBU2dnG8oFIPJ1E2Qy7fYI1Vwv7yXv5/9u4rSrbsPOz7f++TK6fO8eY4uWeQBpkmIQKMhrRE2lwyZZO0Sdo0l5eWZS+by7KeTPtBkhcpipQDg8glWjKYDIDIaQbAoCffPPf27e7buStXnRy2H3o0IMbEULR6ZjDA+b1VV9U5Z/euqvOd7+y9P79s0dZsnv/qIVGcsXWmwYfHIac/e4u4PIX7dAeFIrF0PrlynsA9yvH/yGzK5L+6TubGCFNj8qfP8dCJCW680MDoD5ESrOI3R2+KJ68hKjZkKbo3In37JXr/8AmyjodqlIkxMOdPoB/2CGvT1B9aPnpjmsLvfAFeWEe+sAEXFsDR4as3YaGF9oOXSP/kRaSlE3gGtF3MzoBsFCLLx59wy/yE62sp60OJrFm87TBl8pwgjTJKgcfEvX160sKUISVMqnoXfeSilMFktEl/6UHSSpnh2XdSuVTiyq9ew0WRpRntZ7vM/+gSZeFR/OAE24GNuTdit1LGXQsx9hK+dH6JQcGmcRY+EnSw9n3UgoPvpzRnYgw7IQgyVKZQBx5QZC4KWOtHFMopb3u8yn0a3N3UGQ0ESTdEmjEoMMsGTFoMEkFjEFDtB0T/m8cgs0mSkEwzkGWHr6gijbkpHn9MMFnX8eZs4lsuTqYYaQX2n+8Rhxl2y0JUyviZzXIxI01BPLSEMRyjvASvGxPf6iHPNrBOTx17X+Vyf5n8LmUud7ykBFNl9AKBIaCgHU2bX6pr/JOP2GwNMpbqkonS0QyUi798ia/945uQKC7+4lkAGhdrvNPfZNBVFHWYfeDUm9aeXC6Xy+WOSx535l7tuypBurq6egVgZWXlr/qsfxT4o9XV1cOXX/9bwE+TJ0hzfwl5ewvrdJlo2EC8tEX66SHRwgLB7QsU7tPIvn4X98BAe2oXqRLcsUXhf/ok1n/7YQDiXkjv4xsIJZj/lUcwphykpRHveRz+7kuoJCOxdOI9n3QQUUwCjHAbwpC0VqI9sNi+OEfW8xGjmGtpgdMyQKoUvWWRjmKCuoMMv1lh/toNj5Z7NM1fRSn+tR4nPlig8F++G+8TLzDxjgRciyzIMB+ZwbQ6JNc2UX6MqGmk+z2y5zePNrbZJm1Okj4wTVqtoGp/Yd2xzTZsHB5Nu9tow04PlifhvqWj/939c4jLM1hLVxCfuYVoDxFdher68DokSLsDxd2+JBoniEDx1BrUP/cC+4cpE3t9JnsjTug+Zuay3jiNVhPoBZvqxRruYYhZt2jvJ0SeJF1P8MKMVCmiggm2RmFnC/HbO1hXRizfLTOQFZJbIwZlk0DXIVBoNUk04TA8WWH2qYj0xi6BnzHuKMZ9wURZkMYZpyKP/taIjczgQVvxgWZGdK2LfSrh9KMVnv1cgN2yKUw7xOOY4lyR7oGPHMaMDhOwDUgCRMkmdY8ugvumyfjZQ4aDlK0Jh7cvaSxfKBLcN4dmaujbLo4SqEFEFGRYUhALk/F7H2Dqo8voEqprq+xdH2I4GswWsPeHLC/NH3tf5XJ/mXyqUy53vBSK33CLdBIFAvy+5Mdefm66IpmufOvSPH/8uTEfVy0yCXc/4/Kf/WyJaJTgnpkiTsDTIIr/v/vJ5XK5XO6tJo87c6/2XZUg/WtYBDb+wuNNYOEve+HKysrPAj8LMD09/fofWe47z8tT4o2yQBmKOM5QO23c33uOWMVEz+wTbHrITpFRpYiW+lT/6RO0kgD1Y+/k3j95Cf+lPtLUGH55D+d8FXO6QP0ji0z9/EWSdsDZWy5PfbzNql3CSlLMez6PxT3Y7lKanWd2c50glexWGkzLiFCzCVITrWEfFWmSGjSPih2V7nQ5+dI23o0e9tkq0pBkPZ/h71+nPFNk8mfehZBHZwOlFOwMwF9E/9SzMO8gzs0gr95GokjDDGFqlM44RO0RbHcoVZvgnzzKyO10oTPC//I68aHAciKsC9+6tqiQEnPBId08QCmFrifIO1uwVDv2rkq8lMA0iQs6SsH4+gDlpcRxRtbUqXh9TE2RZRrUikT3TdFqtLGKGv77T2KNK0waOoerbbZe6GBUbMJeRGpolJbKbL04YGJWYfV9Sr2MQaWIlBnGOCS0TTQzRWXgTNrMXXKQhxNkCJxbbcz7T3HKTjn40i6pYyC8gPvubLPQU1SmbHZvCzaHiq9R4oe797j0Qxc52Mvof/UAu2IgTEkaZQQRJEKi3AQZZ1iOhlU30QxJPxI4hiDYd4kzCKsmV/6vLjUVIzRB6WSZKBNo3ZDhnRGl5QoL75li5r3T6PbR5/zCP3gQ7VdfINI0/BCCrSH3/uQep//2CfTC9+opI/dG2e7m9/JzuePUDxSqF/Lo2gGZLti2p4Fvv/73x54PUfsuQsGfxYqfyzL0yQLlcg9nnCAtiTFbeuMakMvlcrnc6ySPO3Ov9pa62l1ZWXmGo+TmX2ZqdXU1/TbP/f+2urr6m8BvAvzUT/2UOu7t594C3n4WdrqI3R7ivjnSAwFxBllGcvOA8PYAFSliJVB+SEl1UWUT72YH+dtfZbxfhEyh/Bj3mcOjRNcgAk3Q+lunMBo2U/2Q2B8SJRky8vn8mZMs773EvB6wHK7jbLiM0Th730kuffiddD6xA2lKuDZ8+WKlyPcMBEqWAAAgAElEQVQ/cZPOo3PUr24y4UA86UAGxWWH4F9cAUOin64jbB3nHbMopUj/18+SfGMThY51chaqBbB0om6GPWjT12xGsw3O/MIDyF/7DPKcQh/sw8efIdkbIA76BNd67K8XUImN8jUaWyalnRAjPir8AGBMO2iPVcnCDK2iI7zgdemq4ukyJUcwiAWppVNzwHJTwkFA22jA/SYNYqLZKR75T+5j9vEpsjjlzh+s0bs14uD5LSibxKMElMKZK+AbOtl8Be1CjWw/IlMuhiUpOworSUlEhIwU00EAdYN2U+P+SXjsjIk4fZbx1hwv/qOrhHspduRRnLIZtSOCF/dxfRBpQrreYdOqcvXiNImpMQ4t/pv7i4zXugQHHmE3JKqYRKZBlmSITGE7gmwY0arqyHMNspJFsOaxM1FA9BNaWcTg6320OEG/UMIsmyx/eJ6tp3tsJRqlRwtc/sg0lRNlsihl++kOa7sxpd95BqPvo3UD0lYN51yLqB9x8I02s+99424SKaVIgxTN1hAiv737vaLVekuFJbncd7yGLfnhp29ibY1QAlbwgUtkScbWn+8w3nIpL5WY//5ZhBS0tnuUDscAjIIAmGd61qBkKAajENM0WV4239Q25XK5XC53HPK4M/dqb6lPxOrq6sPHtKlNYOkvPF4E7h3TtnPfZZSUxCdPktWmGHz2OkHnAKtkkLSa6Bs9NFOQhgpl6KRVB1kuImfLqEzRXnPpBw7pKKNhCYySgV49urDI3OSVfRQMD2SEjkLYOkYhQTxQw8lcwhc8pps6BBFpZ5fw7hDiBGXoqFTRTzSGX+8hg0NqT+1h1Ew4V8WYsLGXy6hnN0h7PgBRmCHvn8R5B/C5KyS/+1WCQ8g0jaFh4kyOiUOLwKizXT/BxxaXEYsVZl8Q/ExBor+8UsvaTZcX9goIUeCcCND0NsqSbBQbvKDVmLoqOffrz9Kp1lBJxszjLazQJLnVgaaD/I9OUHgd+qq6XGL+Q/OUbg4IvRRHpZAkJC2Te/UKO8U5mK/w4AQctlOmk4ygF7P1+X0On26TuAmYGnrDolAz8b0UBaR+Qm87YOn7zmJVO1QvLaJf0xGfbbPQPxodrMcpjw0DPMNGW/O4/mu7FOYdBrdG9K8NSP0Ua8Ki+WCD/c/uMN6KScYxNjFBrHBCj7m7fbREIWsGfa3C+KvXEUqRpRDtBdTeVydVEPVCSr5PrAQCgeyOEQ9NMWNIJiUETRP/Shd/28XIUnZ2RxTni9Tvr/Nso87tf32L1E24+cyIn/z7p2h/cZ//c9ugfn2f89eHVNIEmSmyakph/uUlFd7A20NpmLL2r9bp3Bxi1kzu+7mzGMVvP+Ip990jv4+fyx2vNEx5PBmxV1ZoAhbDPgDdK336NwcA9K71KS4UaVyqcb6WcXd4dHlwupQihGB0c4D5R9epjGJ0S9J9xKb5t068aW3K5XK5XO445HFn7tXeUgnSY/SvgS+trKz8A46KNP0M8Ptv7iHlvlP5H7tJfOWA4UtD3ETDf88K/n5IoWjTfNdpnIZB8pU2QtOovnORePIU5fYuSmj00zp2WZJO1im/fZLqtI1/pYsbKLZ2YrZ+/SanPjCF5St+YHHInlbDkzb6VJUrCydJT5U4+Rt/SvqnVyCIyLopfOwZuBeTnpwkKpqsDzQKXoomBIPDCGwDlYHmaJQeaRLe3kEWdLqdhIGfULzlEX1pn+n1fRJfkKaK0I3pFiusn36Ys2vbyLbLi+dbBBhkHmy6BhsnT3J27TZK13lRTpHt3gXgbmuCE/WIjjA4KNXZOT3HDibiC9uUHrdBCLZ/5xZzlRajxSK7GzGDf7rO1H9oc98Dxb/iv//XpKBZk7TbPpWaDsOIQS8itQxSTaJJQZgqtkcg2n1e2BqQ9kOGLw1I/AQEaCgqDZOTf/sEtz97QNCNsSwNmSZMv2MKFpcIbg+p6h2yGyGJ72J5IaauKEzamGeqDO95KBQbf7qFt+2R+Alm1aQw5TDedMGNMSwDszcmTHWUpZOaFtVRTNgs0DAFL/4Pz2JvjQn3A2JdJ6raeIcRrYfqZDMO6ZcHZG7GaBgxXJck6zexmhaVpRK6FPQyQYikMgxIxglWzWLn+T43twbEw4Q0VezvBjz5B9skt7o0XIk28MnCjDjNMOIEd5QSXhtSbZkYt4c4kzb1i8e/NMKr9W8M2PpGB7cTAS78H3d4+BfPv+77zb350ny0cC53rKSpUV0qom26IKB6ugJAFqXs78d4XkahKJmNjyZh1U5X8AOfTED5hIkQglu/d4fBICETEhEorv3zW5zJE6S5XC6Xe4vL487cq31XJUhXVlZ+AvifgTrwIysrK38f+P7V1dVrKysr/yOws7q6+hurq6trKysr/xD42stv/RTwe2/OUee+06V3j0ZbRMOYaMvlbtfAHyRY5zN245iFe4rKVBnzTA39RJni3zhBWnmUq3/vSfq7EYVaRmHCofhQkzhVGD9Q4t7/s4f86jbjts/ot15APDzDTNHhlxZ3eKoxz5/UJ3gKndXnIn7ykRVO3tgnU5L4xRHu5zaIGlWU1ePOB1ZoXx0xeeiiJynaMORuscJT779IVjT5/lnJ4iMzIGDnGRf7XBNRNGg/22H68iTauRbxMz3ioqQ/NcFBoFEPNUqmBe2IURIj4hjtKy7Zvz9L8r6TtK8PUb97G1UrIrwQlYFftVEbIYGRctiP2ZidJJ5J+UCaIQ7GqJ0BiZ3Qvh1CJ8Lwdlg/iJj+e+eZuFw/tr6686kd9p7uIkYh7iggOQhQqSKJUtxUoM40MYYhej/A3Ruhhh4okIZEU6AUCFNiNS1OfXSZzu0xg90O6SjCTlOaNUHYC9n8sy1Upihoip5TZC6MEIYgmK1SqukcPBeQeCnxOEZIQRYrRuMELVLohz7jzTHS0sBxkEmKXRC4BZMiGo0SiFFIPPIwahaJlyFTiM42MQ1oXq5jzRTYHgTwfJvBQYwfK1QQEHZC7KrJJ6oTaI6B5UADjRNJQJoqhBthSIsgzlAK9CSlvzZiKk4QHE3hvzM7xXK7i3Ik1EqUg5jCZIV4GLP1qR2K80XMyus7mlNaGn7/m1VAxu0QpVQ+1f57QPJXvySXy/01SE1w6RcucO+T22iWZOmHjlaqihyTng/+ICHSLBLnaHbLHxZbPNOKUcDtis7PK8X2XkwsJYkm0bKMQ+9NbFAul8vlcsckjztzr/ZdlSBdXV39A+APvs1zv/Kqx/8M+GdvxHHl3tq0kzXi338W63abYVIgdhIyKQgPA4z9MX6QYqCwpssA6FMF1v9wnXTbxYwU4W7E3MM1Dlc7qFQRxRnR6h7xYQCHPmNAPd1m0LTJHrvAs9cyDns7KKBQM1jTfKppC50xN2WNK6cX0ITgfBaSBQlp06I9XSEJArqLDncmG0w/sYFXbvLSExn//QenaLxrAf7xDbq3hoidkIlHJ3DPnGT0+JB9+5A75QmixUlGL464Nz9JPQuZuXrImVjR9xzesb9L7Yttrr+4yMFX97HWeoStApWzNuVbHeQgxNRTfFNnab/L4f2zuI8vkvza55AHI3RS4ikTfT3Cb1YYxBJ1u8fWJ7eRqaL5QONY+mq45WHVTVLHoLftY3dDDAkFU3GmolgTGTUtY1YljKsGve0MEoWORqlqQqJwFgq87X95DM3SMGomRV0RjWOEBxtfOqQ046AyhX/go7wEiiY7i8s4vRHVL29hbfaQ7QyvWSP1U4QjOYgcekLnhqjz/ns9SlIQ+BlupYrUBYmV0Zgpos+XCNdHWEVJXXpoXkC0UMQdZCS+wipqVE+WSXWBtlBGV5LoM7vElkYmJAoY+hlPn53Am5YYkz6XvAHn1jZJ3JjhtT7f9wMLfGrbgGFMveHQnNCQtk1lKyGqWsyessk+E5EEKbZSR0tBvFzkWGWKNEx5rQIfx6F2rkLjYpXe7RFm2aB5qZYnR79HpHk353LHrnKyzKWf/9ZR+L1+Sr9QQDkOoRD0+inTwPN3IqLgaNLh9c2jtVXM983SvT7GSFJSKSk8MvNGNyGXy+VyuWOXx525V/uuSpDmcq+HqNfHv7uBTCJKhwPqfoGN6Tp3lUXZTBlWbM65Q0pKUvubZzGWqyReQmZI9DjBMgTbkzWeTwpMk3A6HhG3fdSBB2lGpGmoICXqRrzwv9+h3E9J52dIlCDalzxnpYyjKcoHBl87MYuRpVgi4VMT0wRDycxmj6yg8+TyArEluVloMjt2OWsmJJrk3md3qfzd02hFA7tpozJF0Au4+us32a02+cTsDLW7PYqbbeZlwliTjA5TCrrNYnvM4u4QQcTtRspeZwsEFIoOlcMh761scKM2T6SPEGFI1fcINZviwKPyp7sUfA9JQpJCV+n0LzVYS2zUgU+xIInaAe6Wd2wJ0sp8gf2XxnQqZbRIkroRepyQJgr7RoczNzoYUcI9zcRPIE6goBJKpqB6oUmx56MUdJ5uU/iBOeoXq+x+o82gXmBHmQz/bJ8TjzUx3BR3bUSxIMlsHb/rMzsaoDmC8T2XqqNjnS/T2dAYKcmBoZEMY+QoZucg4pwm8G2L2NQpT9tkuuC+nz3J3NtahN2Q7V/9Gs5OBxFENOclty4scKJiUi8J1v9gDX2+SOpn7MzUuXsiobA3puoHICSHA0XBFohhTFrWsU42qel9jLqFVTHQByF/56em2Hh+hKEpGmfKvHAzxpmOqJR0yrMW8cUGWphgaYrCbAHd1knDlPqlGs6EfSx99VqEEKz80gX2X+ihMpi6//hGGee+syXkkWoud9yyTNHZj9A0QWPyaKSoqFhIRyNwE+yijigf/d3p+3jFo995axySZRlzH1rg418eU9ke4tYKvP8nT79pbcnlcrlc7rjkcWfu1fIEaS73Gnw/4+4n1tAPLZKRjpmFRDLjRqnEN5Zm6M3CdByyMhrwHz/QxLrUAqA4abI1TFGpQpsr8cXFRfq7EVeSjOSZe5TCmEAXpKZJpATEkJBi9EfYccb7+ncZGibFJKG31GQ8SjHrFZSl4yY6PaNA3yqycmuX0M8IpSRKNXZKJSKhccepowvFT6g+29KgEUrcBHqWRdQOKN0YErUDVpsNLKVRGbhHDR64aJZBkCiiVGAqgRASVbC4KRsk7gBNQih16pnJF9YnyVRMOt2kaLosGxnXNIv7r+9wod1G3G4jdEGWwFrmcGvCINMVDSnx0LGf67L840uv0QN/PXMP1dn4813sqwMiKYgSyAwDrW4R7vpkqcL2IjxbQ7c1PAkb5TKzeobc8VhPoD5hUbrap/VggxPvn+b5J3p8JSvjexm3LMmPI5meq1DPMjRT0nATvH2fWlhD3x+hlEJJgTI06hdqWCmoJ12iOEP3Y6pdl0iHYdUmMAza0uZsSVE7XWZ4GAGChVkI3SqjYcbd0GGcSkwFiZuQZAod2C45/MmejXzHSSrP7HLf5gGVso6hZ5y8us1+7WhE88NNmHnPNN7eUaEus2qw9IPz1M+NEEKQVCzKcZ8y0H9yj80XOzhegPBiqlUNlSgqyyWm3jVJ68HjSWT/29AtjblHW2/Y/nLfGeI8Ts3ljt3zXx2ytxUCcPpikTP3FSlPmjw1N8EwhJoNH5k8mhnQ0lPuTR6dPxZ22yilsA49vIk67WYdU4C17cLrUmoxl8vlcrk3Th535l4tT5Dmcq9hMEiJPAN9nCESRawsPNNiXC0xmabMDofgjpjf2OWJf96n9+ktCraGe+DSnDVQfspNu0CmgI6L2vcY9CKWRh4jF7pTRWKlofsp26Ui64tVql7IA5sHTIce47KFSBOcwEeNUmYqgtvVOnvlIrob0XUFwraY2usyWS5ze7KBoQTnrYxlEXMv03mh3uCPPxvS6heY3OkihimVAxcniKj6fTq1KkKBYUkKQUQaJmSaPKrm7qcUVALKxFsfoSwDPUiIQ4Uxygg1MC2F50k6k9MgwR1lqFgwcFOMUgEFZFGGh47hJyRCsF9xcMoGwk5eKRhxHHa/sA8dHxGlZH5GaOnYpk4QC7SyBf2ANAWZZLiZxpNTE3iOSUFTLIYeh46DMCUf7GacszSkLin86CnKT7mkeyEUDF663adiBcwtFol6EUbZoHKqTLhXwnB0Ji5WiBbqqLkqzfvquAcBq7ducU0zObHXpSYzsvkKMpWkVZtMSrKlIgcbATs3RgAsuxJjx+OuX6QvQZ2yGJUMlh+v0X2xh0oVqx3JIBVkocBuFUkHNoUSFMoal70RdlGnnsSc62cs/cxl9p484CASWA+2SJOMoB0SezHNB3Qmpw32N3zSto9d1BDSQI0iEkMjdmO6V/rE44Tq6TJGKa8mn3v9hPmd/FzuWCVxxsZtn3Y7QUjQTcmZ+4rcSzSmChmTXRfZLLEZakwDt+cnENnR1PrNqQZCCDqHMaQZIgUhodfLV23L5XK53FtfHnfmXi1PkOZyr6FW03iOKplWQKgUXzoEhokhBKXhGCcIKXojzg/uMhxGDPYdkrrFsBNSnDEwJiwmzZRnvraLOPQwvIS5G/sYWYyZSawwJTU0uhWHz59dQs8yNluCxJQ8fG8fv2pTH42p9oeMM4NrrQJbhkFh6NGKE/oFG11IJpG8/+oGVpiyttSiZAvSLOLFRKKth0RIRiULWS/T6AcIPyEFZg9H6KMIqWBBRkzXIF7v4Bsmnl3EckOIM0QvoFVK6MzUSKUglpJDw8ZQGVU3IiUjHgT4qcSrFRCmZKtZJSpKnCjBbHvESCpexMgykKOQmq2YWGlgN6xj6y+VKaSAcl0niiK0mo21WEQlApFlSAmmnhFPFfmTxgzrxSJzSQBpwlaxQtEAlSh25utYtaPphudPWHzdMymfTtl/pssJEdEsCuJRzNmfPoNZ1pG6JA1SPASOIdC1b55sywtFTl6sMvPiIUk/Q3ccnJUWu2sxnG6ga4LGlOTgjvvKe9Yn5pmd9oj7GlGxgkwUhaUijZUW5aUSe892kZkksqvItk93uc6jDwq0fkjjco3CU21Obh4idcmJD5zGKBvs3T/Lx+8q1Kai8cUulz99g8RNyOKM6ccnWWratB4qs37LIwsEsmrhFDUYH61Fp1JFGqR5gjT3uoryODWXO1ZSg739BM87qlJ/2DlKbhoHLvNf2gClUHfbGJdPw1yRodDJXk6QjqVASolsmohogJmAJkFVi29ae3K5XC6XOy553Jl7tTxBmsu9Bi1OwdQYlmowjnALNtfOz9EuOzSHfWYGfS5tbJElJaRIyIKM7cOYfc3mut1Cr1g0H2nynj9+nnAQUuh6VEYhw4LJeKrC1mwVuxfSLhbwLQOZZQhgu1nkgyJm27TZ9QwONI00EuwXCyghGGomqmAzoxIW9ruYUcLQNri8sYdGiswUc70hTy/PEJkG2Bq1JEalikwKumWH0igATRJJie/YOImkrZmUJkBLFd1KmTN+RF9pbDerbExWqYYhc90RuiYRGURCsO0UKGaKTDcIpKAURAjdYLzQpDTQGB+6qIZNkGlYSUoliYksgTnpcPmXLx5rf828b5rh2gh1vc/k5Sojy6YwaXPqTJGlx5q0n+vSuefzCaNGMdAwXMlBbFDt9jHTFKvmIA1Jy1QcPt2h9XCD5argA3MZf35HccJOWGwIpDhKpOrO0ShTpRT/982Uq3spBUPwUysmMxWJyhQ7n91lIgs5aFpY9zeZn9KwSzrBGYP9/YgJR3D2colRO2KwFyA0QWm+iGicp3BryHA/wZ6wWXqwilMxcCoG5ZMlJp+IeGSQEeBzbn9AujHCni9g1S0e/a8vM9pwsZsW5cUSAE/vK5IgJQ1TXlyPWAwVxijG74YMN11qmmThsSa1cxX63QRHJUSbY8ZbHoUZh9qFKnbr9V9/NPe9LX2zDyCX+y6TZYLSnE37XogmJeXJo5uSy2OXZ+KUtquYKgkWRi5QRAsSpBAggEQRxzEzMmVvooDrgzQFC07+Tc3lcrncW19+Nsu9Wp4gzeVeQ+wlaLbOaLqM2w7Zrxb5/OwkLT8hK1dodbroKUTCxDWK+Imgg8az8y3+6KHzlBom56oCWany8EtryDRjVDcZVKt849wSYcNgxjvE8mKcOObmTBMjzZgduexHJW6Viri2jlbNSDOFShRapshMQVPLOLl2iOMGJBJCqeEkMY2eiy4kqTR4cH2fe80KxTThROiDlPgqgyTDNXVGhsnYMCii6JoFzFQRmgWkAc9eWqQ1cNnExNd1duolNo0qUz0XLckIdZ1UCoZFB82PSAMILQORpMxdPUQqKKiMvcUSHUNHR5BKyXqtQmhILrsRT/zOPT74S6eQUh5Lf5XmizzyKw+SxikoQegleP0YxxZYVZNSaYLgk7uknZTiIOKUrnMYZHxgIuN8IeO6rTD9kId7Q3a/CNKQFC7U+NKdBD1VjCfLPHE34vsMj6l3TKDbGgDbA8XVvaNTrBcrPvu8z4fKIdEwonulh63B4pzJ3Aeb1M5Vcbc9Plw3CeOjUTqVCYsbf7hB/9kOxYrB2V86y+HHR3hf3qNmSC782AxTD1RfaacQgr+zYvLk1wdE+wfYd3u4YUpwGHDwtTZTb59g/vtnX0mOAhTGAe3nRpAptHGEmaT4bkKcKnq7PqJgMN+wiDWJuOcSCMHCjywycb5KGmaYlXzkaO4NIPJb+bnccdJ1wbplc2AffbeaxaME6ZrlkCQKR4MwhQ27QAt4150tOo4DQDUI0PXTjMo2lUEPOUqwTMmgNPFmNSeXy+VyueOTx525V8kTpLnca7CbFq2HGhxe6SFiRX3gc36zzcZci7uNMmmQkiUm5w+6HNbKfOXkDNuFIqGUoBRdpXG3l7H/zgfZKVeY2OpyZWYSbIP6hM0gyUibIcWxz2N3d1joDFnoDJjvjclQ9GdsNKVIhcSMU1remE6pwFkz4pHbO4TDBLKMSGhggpKCTIBnGihNMtsd8sD6PpousSct6LqkUuOw5JAliv1ykdo4IDB0egWH0NYIdEnSKlBddHh2rcn1coW9WpFalGBHMUYckwlJpGtEukZz7OM6NkacYoQRh/USk7ZJdeCRCigc+Fw7PUfkWPQtk7If0a46PNFsUrvRY7AbUp9zjqW/xvdcXvqXdxlve8y9e4q5D87Q/twO3p6PijOUUpgVk/f0x3y+2MBJUt69vcU5PcSZLvC+M5I0O0p0pkFK/0afuGWTtQMoW1h1k8riLJce1NFeTo4C2DoIBLLnkbop/cMhO2aAf+Cj2TpmxSDohnSe67L/1QOCdshoY0z9co0TP7JE52bIs99wiTSbvgtrXzig/Yd3QQqkJrj7u3dovXuaw9U2aZjSeqhJuWbyaAu26pLOPUEcgL/vYzVtlFIcPNWmcfmb1d/f1ukwVimBENzf8Kk/2GDv2S62rZGaOrv9jOCLPZLrbSSK8J7L4ed3OPfRJZZ/9PgKaeVyuVzujRMmimTGoVIxEFLgVo9C//2iw6fPLVDueYyaRc44R4nTH7tzlztWESUEp0YjlDrF3l5Ie5wRR+ApxfpGABzf+uG5XC6Xy+Vy3wnyBGku9xqkLin80DLB79wltQwypXhg84CtqQZmnHJYLaK2JaFtcmOqgavpLLcHJEIwEYQ80zqBP4pJkpQ75QrfWC7iGjpFpeAgoDpwabSHNMc+VpxxaqeDkYFQRyMLL24ecFAr0SkVWOj1uLTbITE0ik2DZBBBBsNyEaUU46LFpmViBzHrjaP3bJUdVjb2cCTQ8YliKCYRodS43aoiU8V2uYhbKzMu2ewXbboFE5oO59pd7o8yJvtjuqUCY9tiueuSSImuFIbKUFJnbaqOUiDTDIVgWC1izabMGxrFsYdXNBnZFrZSyExRCiPmD2PuCMmvLS9y3tV4zzH0VewmvPBrN9h58gAUdG6P2PniHv6OB1KgWRqaKTEvmjS9gJ/UDkET7HUHxAUd/yCgcqpE7+aA1E/IooxoFHPvz7c5VS+xr1u471niXSesb0mOArRKkveHfV76+j6aG7OgJXCmjNW0yaIUb9cjaIegFOMNF6EJ3F6E57aJ/Yza++YIY4WQAgXsPN0l6UaoVGGVdJxph+3P7NC/MQBgdGfEub97huqpMu2WRfV0hfGWi17UqCyXEUJglL71573SMHlctI+S+mnGhZ87h/7JXcJxzEsvBSgvofNsh3BzjOUGaAJSS6N7pUcaZ8w8PkV5qUQu97rKb+TncsfK0gUna4K1l0P+C82jv+s6PGOVGTQrNCyFpR/FHa0zVbjWB6C2VETTNMZ3Rszt9OhaFqUkJr2eAJNvRnNyuVwulzs+edyZe5U8QZrLvQY/Vvyjz4U8yFGRn8gyCS2TUpwyTjMWOkMyIDY1+iWbQpygoRBKMen5FA5dXNOg2fNY3Dhkb3KCihchgL4Gum1wZWmK+sjn0Ts7JJrETNNXfqtP73WZGHkkUoAQRLqGEIJbepHNy9M0ei5WmrE2UUNJwYN3dujZJp2CA0rRGPkEuoYtUraNAnHNxggiQiE4MEw0W/Lc8gyHjRKREMRCogEFIdn3wNcMzu53me+N2Zmsc2Nhgq2qzcXtDsrSubbQ4rBSYHq3TyIkhUwxOXZRKiU1BFHBBCGZ7g6xpGAhyUgEpLrOycM+5tk6f7opec/Zf/e+ikcx3kEAR9d4JKMY/zAAKSBTpEGCUTpaQ7O0UKS8XGJwe0jzvgaFWYd4FNO7NqB6qkz3ah+rYZF4CfEw5vRsxnIxZXE2ZH6q8Mo+Ez/h2d++y+4dD73t8dC5EirS6V4Zo06VkJrg1H9wioOn2rhbLkmQkgQpXj/CCzJsIdi9NoCKQTKKSGNF+USRQhphrDTpvdBDaYKz/8UF9la7r+w3GsakQUrv+oDES3BmHC7+p+fI4oy9Jw6QpmT6PVOvTPu/MCmZescE3r7Pzud2sVs225/e5cwPzrJ/ZcCa7yF2xzAIkQUDPQjRdIFd1Om+2CcNM4L9gKUfXqB6Oh81lHs95Uo2WSoAACAASURBVJFqLnfcHp6Cb+xBQYP7Wkd/+9K2YJxpSE0xSCRf3hW8/zQsfd8chYaJShWN+5sopaiKjMkoYnbskemSqn48sz5yuVwul3tz5XFn7lvlCdJc7jVc3U2wntiiXS8wuTfAsy2G5QLLB31avREntvepjQP0JOWdVzdoF2xiTWOnXMAtFKmMffyqxqTrYWQZrSDAlRpCKWwy2o0Kyo/xTIOaF3BmpwOkZBz9XMeaxnatzGxvSKJrHBYdJocuzzXrCE2yt1BgvVnl8Ze2KI0DXENnYFtkUqAnKVaSEOuSoOWwmxjEBQczTOg4FgcFi716iV61QGjoGGFMKYzwTYOxEGipwokTtCyjGkTsqQyZZcSGzpMXFjl/0KcUxFxtWfiTdQ6KDmcOejy8tk3VC3HCGCEVCoMTBz0CQ8eKU6w4QauXiaOYDU0weUzFcO0Jm/q5CqN7LmmQYjWPRlaSKbxdj/qlGpOPtoiGCe6Wy3jTpbxcwqpZDO8M6d8cgBCYVROnZZEGGZqlgRTotoZtCmot81v2uf7pXdafGwLgHYYYlmTqbImpt08w+bYJinMFyssl/IMAd8tFMyWaraEMSdKLiQYRzqRNtOtz6YxFz4P5R6tYh5LOCxGlizWaDzaoXm4QjFP2nzwAoLRYJAkSdr+49/KRpBw81WbxB+c5+dFlAD72YsTzOxEAl6c1PvqAiW5pryQ4/X2fzE858b4pttu7XLsxxNgY0zBS9JJGsWkh1nsYpFgTgqxpMd508wRp7vWVx6m53LFKM8Uvfybj63sKKQS9QPGbH5KkCAxdESQCR4ckO1oLvDrrkHK0xmi5ZSGE4G3vqPLFz3W4lxg0ZMoH3lV9rV3mcrlcLvfWkMeduVfJE6S53Gtw9sfUumN80+BgosawUUHZOqafMDXwmBh62FGCACq+QotT9qpFdFOyM1EhNjQqfgCZouGFPOrus1stYqmMVkHjG+UaI11Dhgnrc0fz3i5uHR4VYhIwciwKcYKuwAljwjQDKRBSkAmBZksm/QA7TckEmEmKEyc0xz5jyyTRJCVHYmYZlSxhnMaUxj5xkiBLFq0w4sLaDt2CxdJuD8/U2KpXGDsWhTim6fqYWQZC4GQJVhLjWRaZJtGyjIofYSUJThQzEyeYfkTRC3GCCC3Ljgol6RolJSjEKXqcIBWU45j9ksVCQ/IzD2mv3Qn/lqQmeOi/ukTrsRZBP2ZmpYm7MebgG20SP8GsmkystOhe6dO/0cfb9RncHrL0w/N4ux6Tj00wvDuid7WPUTFwWhaFOQd7wsZuWUw80iJxEzY/vkVhtkDrwQZp8M3ah3K6iDlrU7tYpThbwCgalBaPsr+Tj7UwKgbetksapYSrXcJKhtQFZGAVdXQTSjbMLpiMhYNKukhdkrgJiZcw9fYJirMF0jClfLJM2Am/pf1pmLL3lX2iUUzrwQbX979Z+Or6QUY0jInGMYmfoDs6QhMYZYPYTahsdnngtEa3ozAsncZ9E+j3ekytlHnpiR791Taqr5j/92aPpa9yuW8vj1Rz331WVlb+c+AXgBhIV1dXH3yj9u2GKV/eSnl5eW3+5TXBb34IHpnM+K0gJckgSODRGQCN8+9tsn1thFIwd+FoWZVsoULxIyc4fbMPM0XUY6036vBzuVwul3sd5XFn7lvlCdJc7jVMGYrzUxrXBjahlVGxBI1yxiEZfgyRoaGn2dGLpcAEzhFSHgy51ytzpttmuj+mGofsLTUo9Vy0ksWpzOc8Y6x6wlUEjgXjUoGXDMlsb0QxTOgWTG7PNpkaeIxTi4FtIk2N+sjn8k6HF+Yn0MKMd40PMKOInpCUowg7jkl1yeUgpJHFiJZNf6pKULQ4fWUXFSSkacpDO4ektkHJhNL6mMIowLUMHr53QH+6wlfOzNGZKKMVDdLzDT44p7G7J9C8kPkDl0xKYk3y4EGXM1ttNkpFVtZ2aLovJ+4EJLrErGjsGEXKXoRhHI2e3J+sICoWPzvYxXZLYFvH0l9Slyy9fwalFOsf26R/a8DmJ7YxHA130yUexsy8ewr3nou77QGw+6UD7KYFGdTOVekEPZr314nHCe3VDq2HmsTDGJUpNj++BQr6NwZopmTxvVPsXhvR3o8oLxZ55BdPM3iuw/ZndoGj7S1+eB6A+vkq1TMVBnfGGBpUqzp6w2J6pUH9TIXR+pjiXIHG/Q2Ga2Mqp8qvtCvoh3Rve6Rxxuz5o6n7zqRN88EGnee6R+uNCjh4qg3A8M6I6YeW2Rz/m89xxkv/Yp3ETQi6Ia0Hiky/ewqrZpL4CUIKbE1BmBAF4B/4lAA/VOhNCxGkGIslsig7ln7K5b6tPE7NfZdZWVn5ceBvAo+urq6OVlZWpt7I/Y9CEHFGKiQCEGEMmGzfcjkZpAykTiON2bxlwlkTdMHBVIVUwaxxtI17I4hnSsRTJQwJawPF2Ub+Zc3lcrncW1x+Ksu9Sp4gzeVeQ+VkmdnTRa7dFIRhhiYy+glca1ZpMoYoQyrFUm+IQlAVKZkbMzaLnNvqYgcphlCYcUopijFKBlaUkIUJB17Kqc1DHhcxwxf7SFNwxSgSOiZhwaZfsLl2Yobq9U0OdY3EkIQ1h+eWp/ng1XX+xtU1Uk3DsQXuRJGirRE6NfZiyVJ7CKZEnyxgq4wv2CXGoWBtcooP9da4HAUYkxWcOhw820OMj6ZiVyxwdKhrLqc2NhmphPRcnenzFUYCHn6xzYZuUfEDYlNieRlnD0csPNqg++QeXvBvRjUq9KIOmoanNL5+bpayG9JzLAJbp6YyfmI+wjEFUT86SlAeo2ScMFofk3gJWZQSK4VRNhjfc2k+0ODuxzYRmsCsmVg1k+l3TnLwVJvetT6Jn+BteyjBK+uZqlQxuDV85TFA0AmpX6zxnv/u0v/L3n2Hy1WVix//rulzej/pnRQgEOANRYo0EYUIyL0XBLGAYEO8Fmz32vVaf/aLVxQVxYYFewEUC4LAS0+AhBbS++llzpT9+2PtCScnCSTh5Exmzvt5njw5s2fPnLXOnpm95t3vehcb79hEpmOIzJq+7QspAXSu6GLqyyfjnD/7RqKOgy6eRZAv0PtML+n2NJNPbN9htXmA5oWN9K3uIygEVE+pYvXjA2xe6QO6W54ZQM6dgIs4Jp86kYknteOijqd//sz2xxeGCpw3Ex4aiBMEAXO6e9k2kMdFHDWTq6mdWUvdTB+AjaVjTD5tIqv+uAYXc0Sco3/dAA0vbiP35GYi8QzxmQ3EJ1UTr4uP6nEyxphx4F3AB1W1B0BVN47lL2+tgvRQnlzcn4fqslkA4vkCnfkoQT5gm4uRKvgLYL9YEfDIVn+yW77N8dpDHe3pAptuXQereig0pmi+ahoQ2eXvM8YYY4wpVxYgNeY5ZHuzbMnGyUQgGy3QHXEEmSHyTXEePnwaG5tqOaopoP+pLaTW9/CS2gxbbl5N++AAndUJ6voHyVYnSCUi1EYCNqWTPN3eRDYY4t58hCAb8KKV65jSmcFFHHMYoqMqzWBTiqHBgKm9g6xur6e1o49IzPH0oZPYlE6zodBP6uH1OAeOgGjU0X3pYWSvf5S+7jxBISA6WGBDIUG8kKfbxSlQoCuZYHlTI4s2bKVvELIbMgSZPNFCQOAgMpCj+ehmIr1Zpvf0kU1EyKzvoH9VJ8ujaYLBHO3RLHVdvdQmAqKtaXojSZZuhdoXTaXl0U0Mre8n1jlEuipGVx6yEf+lbFt1iscmNFOfzVKbGWAjcGSjo3py1XMfhH0QTUeJVccoFAok6hIUcr5MQNviFiLxCHMumsWaP60FINWSpOWIZgY2DVIYKpBsTLJtRTduQg3xcEEmF3W0HNXMUGeGbG+OSCJCwzxfg61rRTebdSvgMzdjVc+WDEi1pLYHR4vi1TEOefN8BrdmiMQjJHYRdKyfW8fc1tlke3NUTari3l9v2H7fQE+W3FCBeMr/nkjMf0ltPLSR3jCommxOUduU4MXhPv0bUmwbFvBNt6V2+H1NCxuJJCJEohHyQ3m6uvOs3Ay1h80kPW+IxuYY1VOraVzQsE/Hw5g9ZlfyTeU5GDhWRD4BJIBvqOo3d7eziFwBXAEwYcKEF/zL+3KOtniBpp5eIkBdu//8b2xNMm2oh74c1MYdNU2+vvRjWwKWr89RCAIGs/4i2/IHupl6+zOkOgYZqo6z4q9pFr/WSq4YY4wpczbuNCNYgNSY5zCwaZBCxNGWytGVCwiyeRr7etgar+fgdJ4p6SwtLVW4fD1VSWiKwtDMWgpP9XBITycR4IgTG2lYOJUbYg08MZBk6qMbeDKXIh4UiLs8982cwEEM0rNukMF4lP5ojEh/jpZohGkDA0zv2sbTrQ082txITyLBvGSWWcc2E398A8lCgQgwe0qc9uoCdw/lmbS1m/5YjLhzuEyOron1ZIkQLeTIuQjRbJ6hdJx4U4ogn6cQi5DLR4gEEMSitJ81lcjqXjpXdDO4aYBcf0AwkKe60Ee0L0s6M0QhHiHmHNmuDI4IifW9DG4ZIDq1hraqGJk1/SQbEwys7WNuYZAHBodYX1eNA+L5PIPtNcx7SZo5R1URTY1ODdKiQrbAqj+sJdubI8jDYe84mEKuQKo1TdvRLWxa1smGBzqgJcXEhQ00HdxAJB7ZHjyM1iXoaaohIlMIcgWamgPmHttE1YQ01ZNm079hgFRzantgM9M5tMPvbz6ymUKmQFAIaJXd12l7vqzZZGOSZKPfp2V6mtUP+6yfhvYUsaQPivav72f1H9dSyAdMOnkCB108i5W/XEVmyyArvvsEM8+fTro1RdWENDPPm87qX68i2zlEzyOd1M6o8YtQhWpn1JBqTTG4eZCeTIHqKSlwMJBKMOWsySTSo3ucjNk1G6ma8iIi9wHTdnN3OxAFpgInAC3AP0Vkuar+fVcPUNVrgWsBLrnkkmBX++yNxqooh8yv4t51/jP95Pn+/DEpFlBTnyQWOKpcwOS4/1Xrt2ZZ3+mzSRMui3MpOh/vpWGLn8WQ6s4wsLQDsACpMcaYcmfjTrMjC5Aa8xxqp1czpQE6BuPEYzkmTEmRGIzh0gF5l2HbhHoGYtAwu5rLjoJkroZ5l87myZ8+Q/+WIeZdNIOaiT4L8eXbIvBUAVfXzOo7uinUJKjKZUnkINWQpDuaYOCwSQQDBTZvGmLTpAZcLGBqS4ajC/2sbYizLZpj2kCGGadOpKN3Kt26iVRLmqPffyirNha4Z0INydVdDEUcG1oaaEo7JtdHyfV009sxSOPgIAt7e6iaXs1QMkoQi5GsjZPLFSikolRPSzPz8EYKh9TT/VQP+UyBZDqKC6JUdQwxmIhC1pGuiuKm1BDZPEB8cAgGHYVUnJ4OR19XgeYFTVRHC+SzBepm1HBMYYiV6RjznlxNXyzKsbPrOfNFrTtlV46GrQ9uo/uJbpyDaDxCzYxaGuf7bM9M9xDP/H0TBP6LYPfmDK1hkLD9+DYGtwzSt24QZjTgahM4IDs5SdWENOCnoxenphc1zK9n64PbyA/kidfFaTq4gXjN6E5Fn3lkA/VtSXJDBVqmV23/u62+eR2ZDh+gXf2HtUw7ewrZXl9TNNefY8t9W5n60skARBwEmTyxdJTuJ7qJ37GJbLZAZkuGxkMaaD68iTkXzmRwawbu76Jzk3/eRCpKLGFTKY0xZldU9cjnul9EVgE/UtUCsElEbgGOBnYZIN0ffnxOlN88FlCTgJfM8ee8GRPinNM2wOa+gNaaCJNa/VeCaTWQzzmCACbVBARBwGEHV7GqLknQlSFIxzn88JqxaroxxhhjzJixAKkxzyHZmOSIS2cz65k+oo0pNibi/PjuDIOdQ2xyMSbUJKiPOwrAutpqtl77CMufylLdmGDJu2eS783yyNeXAzD5hHYW/KODzENbaezN8cjBkyjE4KhsH7FTppOe2kiOKNWFgDuXDVEVC8h3ZbizdQJXzshSv6aPXK6X+1bmeegXm4i4OKe98wgWH+ODf9NqcuSn1KJD05m+ZgszN24mkYqR7ozw4kSewW1DRKOOYDBLZFkn1TV9dLem6W2vIVqToDYacOSFM+itSnHvr9YTNNczbXoNFArMW9jC4z94isFsgcFtESLVcSINKWri0Le2j3y2QCSXZWNNAy5VoG9FJ0N1UaqmVpPNB8zv7aVlQw/kCtSnHbX3dNCzsmmnYONoKOR3TLgJcs8uLJTPBtuDo9tvh1JNSea9/iAGB/J0/KWHwUwBh2PihOcOdqaaksx73RwGt2ZIt6RGPSO2qGlKeqdtwbC+BoWAWHLH3x0blvVZyO64wNK2hzvI5fzj+zcMkG5PUzXB/1twSoKV93WRHSowbWEdkahdXTVjxF5qpvL8EDgT+LuIVAMnAjeNZQM26FZ6/9nFUNTR8fIWmg6qY8bMJAdvSLNlS4629hjTpycAmNoc5a8bCgQBHDUtgnOOyNwGnjh8Eu6pboKWNHL0C5/6b4wxxpScjTvNCBYgNeZ5JBsStDX4Lw53LMsSSUaoak8R7YGegQKpuA9CbfvXBpY+kSNwjqFtWW77wXoObfL1QAE2/2IlxyQcudkx1t3Vwdy7H2MglSDTWsO2lsmQC5gxNQbOMa+uis33baVQE4UIrLtvK+lp1fQV4mzy8VAKATzwUN/2AGltQ4xpp0+k4ZdPUv9kP9XZHDWxPPFclELeUTe5iqGtGQrOkWqKk6qK0TEE1EcYnNtMJoD0ka3cc3cfXf2O/r4IUQfTah1Tj2piwrxaNt23jcfXZOmrTlNbFaFt3VY4rJ7+jf1sWJshX58ktXwrPbMaaJxdRXpzL/GYo21qNU63QAziiRiFocIOwb3R1Hx4Ez1P9dC/foDaGTU0hNmjAFXNSVoPaWDzsk5iqSjV02rQX67HRRxzj2uktjVJKh3llFPr2LAhS21thJaW588GjaVj1EwZ+4/TSadMYNXv1hDkAyadMpGqSVVMOnUi2x7uINWcpO2Y1u371syqpXZ2HT1PdpNoSBJvTdH9VM/2+3MDue0/x1NRDnpR05j2xRjABqqmEn0RuFZEloW3v6eqt4zVLy9kC3zz9kE2B9VQgK1/6eGtB9URjztOPKmWIAh2mM2xvCdCJoBCELCi188e+OszBZbl47SlE3RHE/xieYET5o5VD4wxxpj9xMadZgQLkBqzF2Y0Rbh3jf95Zj3MnhSlM+uY3wRTNhd4yMFAECGPo3cwIJqMkh/MA37hIPIFYlFHoilJpjdHbrBAsL6XoCtDpDnNwlkJmppiNG4OuG5ZhAIBi7s76Fo3QHJqDW4oT6wvT67G16asb9+xjuUxExz/6s2QiDniFHBBhGgqSt3sWqLJKANRh4tFSCYcDkc0GiNIRAjiUWK1caLVCQpBP9XTqsln8+SjeSad3Ei6LUW6LUXdrFrmAENDBWIxWPWbLN1P9lA/p57msxtYsyxLtjODa0zSkgZXHyfd4heEaJhfT9/aPpxztB7TSt3s0c8eBYilosx51SyCQoCL7HzWm/HidqYe14KLOv514zpyQz6zcvk/tyHnTgQgnY4wc+Zz1wg9ENTNrOWQt86HgO19bVnURMuinYObLuKY9srp5DN5IokIg1sy9K3vJz+Qp3pqNTXTbMqkORDYSNVUFlUdAC4p1e/PFGBzJE5P3hEB1o5YfX5kqZuHN/qLlxHnWLapQBBE6HukkykbuygAzV19RB/cDEt2V3bVGGOMKRc27jQ7sgCpMXth4cQoPcv6eOrxPtojOaalG5g0v5balgTZ5klM/Ec3y9b5IF2yIclgIUuuO0vtzBqmXDiTzNIO+pd1UDurlp4VPaQjAb05R2FDLy0H1VJf77NRD2t1fOi0JGv/tJae7CDZadU4B+lUhONf0sBjfTHqm+IsObV+h/bNKwyQnejo3JogSFSR78/TeEgjE09qJ9mUJNkYp+efm+m6fysuHePIl07mwVgNub4sL35RPa0tcRobh1j9eD/UJDnk5DpaFtYQBAF9q/txUb/qfCKsSTn9FVPJbM0Qq4oRq4ox6aSAJx+qp3DnOlJRx6RXTKFrRTd96/qZduYUWhY3Qz6gd3UfT964kpYjmmmYW7dfjtWugqNF0USUoBBQyO16un05cc7t1bm9uDBTujXF/MvmkuvPkaiLP+ffy5gxYy9DY0ZVOhkhO72OFavyOAdzDnruWRELGgLu2eTXLSxOwDh2omNFtMBAwRF3Ace17/92G2OMMfudjTvNCBUVIBWRVwPvAQ4G/lNVv7ab/U4Gfg+sCDdlVPWYMWmkKWs9K3upeWgjU9YO0LNliFWbB9m6eoDFr5xIvDrGka+eTsNj/QTZAlsf2EbnADTVxamZVk31pCqqJ1XRdMZkGlb1MXj9ExSyBdqSUWoW1jD3hFqiw2o9Ni+op2FWDblMnidvXk/fpkHSTUkWnd7G8btZUTzZlKS2NkbVEY1ke7M0HtpI9+PdPHXj00STURL1CaompokuaKBhfj3TXj6FeSOeY97kCIMrMiSqYP39HbRPiNN1/1a2Le0AoFWamXiSrz/mnCMVZogC1CUdRyyuh8XPBm4bFzTs8Pwb7tjE0i8/QlAIWPXbNRz10UXUz9o/2aTPxUUcs49u5Im7OnARmLW44fkfVGGiiQjRRKLUzTBmGBupGjOahvIBE1rjuKo40QjU1Pn3WBAE/H0NrOwKmN3gOGGK3/76RVHqH8lTCGDJ/CjOOY48uZl/PjzAvauytDdGOfOCiaXskjHGGDNKbNxpdlRRAVLgAeBC4H17sO8jqir7uT2mwmT7fJ3G7KCflh0MFcgNFcj05Ykno0yclmL1k4MM9ueIO6gNY4dD4UrjRTXTqpl8ygQ6Husi3Zpi6hkTiMZ2/oCOJqNEk1EWnD+N3IBfgfy5Vn5PNiSYdf4MOpd3kWpO0ru6j4HNgwT5gFx/jkznELGqKOm2NN1P9uzyOQa7c1Snnv0C1duRZduyju33b1vauT1Aui+23Lt1e13W/GCOjoc7ShIgBZg4r4b2OdUAthCRMcaYihOPQHM6Qiziz7ttVf5c9+CmgO8uDejMBDSmHM3pCAuaHXNbIrzvpB2n4a/shm0yiRniv0o+1Bvh2P0z+cMYY4wxpmQqKkCqqksBRKTwfPsasy/q59Sy9f4U1duGGMpBYlI1tc0Jqur9lLWG5jgnnNlEd0eWzlSGoY0DuIij6dDGnZ5rwgntTDhhz+apOeeIV+3Z27V6chXVk6sAyA3miaX941zEkWxIEA1vpyfsvCo6QOv0NOuX95LPFUikozRNTtHTnGRwSwaAVLOvzTnUNUSuP0eqLb1XwcX6+fVsuH2jrxEai9B4cP3zP2g/GovA6PregMc7YHItzG6wQKwxu7Oy007fxowm5xyXHAz/WAuJCJw81Z+D7tsED28qMJCHqjg8tDlgQfOuz089WejLwraBgNqEo3sowLJujDHGlDsbd5qRKipAupfmish9QBa4RlWv39VOInIFcAXAhAn7njVnKkM0GWX2q2YxrXuITCYgmw1omJjcIchWVROlqiZK2wUz6F8/QLw2TrKhNNOY2xa34IAt928lVh2jZVEz2d4sAC1HNO/yMbWtSY5c0k5fZ5a61iSJdJQZ505n012bcVFH2zGtdD/ZwzO/XU2QD6iZWs3M86fvcQ3LKadPJBJ1dD/RTdsxrdQfVNoA6f62uT/guocDcmHW7EULIsxtsi+WxuzKjKbI8+9kjNkrLVWO8w7a8bwzMFTgmU7I5gMSURjMFoBdv/9aUgErtgZ0ZSARDbhogZ3DjDHGlD8bd5qRyipAGgY0d7dsZruq5vfwqe4Dpqpql4jMBG4VkbWqeuvIHVX1WuBagEsuuaQ8V3ExoyoSdSQbkzzfGueRWISaqdVj0qbdcREf0Gw7pnWvHpeui5Oue3Yhh0RdnCkvmbT99poHtxHk/duhd3UfA5sGqdpNRupObXKOyadOZPKp46OG2ZoetgdHAVZ2BxYgNWa37L1hzGjL5gMe2gyJKBza4s/D/UNQl8uSywYkEhH6hvw5vz8b8I81AYUATpjiqE04NvVBWyogl4WmJGztt+GwMcaYSmDjTrOjsgqQquqRo/Q83cN+flpEfgkcD+wUIDXG7CxR/2xGrIs54jVl9VEypqbWQjzqyOYDnHPMqrcTsTG7ZW8PY0bdDY8EPNPtg5rPdDvOnu04KNvPQdv6yOBIUWB2rg6o4cblASu7ivvCmxY5CkHAnWsCeod8kPWM2SXsjDHGGDNabNxpRhiXUQ0RmQhsUNVARJqAM4D/LnGzjCkbE05shwhku7M0H95EvCb+/A8ap1qqHJcvhCe7HJOqYboFSI0xxoyRTO7Z4CjA4+Gai0cnMjxeO8jT2Thz4lmOjGWAGjb2PfvYjf1+scanu6AuCbEIJKOwttsySI0xxhhTeSoqQCoirwI+BzQC54jI+4AzVPUREfkYsE5V/w84H3iziGTxf4PrVfVXJWu4MWUmmogw+ZTxMUV+NLRVO9pKW23BGGPMOJSMOSZUOzb0+aDm9HD1+aY5tRz/+7Uc0ZGlpiVB3ezJABzeBv9a5/c5rNVPx5/d4KhPOhLRgFjEMXc3izkZY4wxxpSzigqQquqPgB/t5r4PDfv5a8DXxqpdxhhjjNkDFncxZtS95hCHbnAkoiDheqPdPXmGJtXharMM1sXp7cmTbIAzZ0aY2+hrkM5u8Pse3u64/IgIui5gViOcNccWtTDGGFMBbNxpRqioAKkxxhhjypizkaoxo60q7jhp6o7bBntyRNIxIunY9ttFsxp2fB8653jl/CivnL/fm2qMMcaMHRt3mhHsErAxxhhjjDHjSOuMKhKpKADJdJSW6ekSt8gYY4wxprQsg9QYY0bJnStzrOkqMK81ymGToqVujjHGGLNL6bo4R50zgf6uLFUNceJJO2cZY4wxZnyzAKkxxoyC+9fk+NPyLADLNuSpTyWZ3mRJ+sbsFZvqZMyYiaei1KcsgEx3WwAAIABJREFUMGqMMWacsnGnGcG+vRtjzCjY2h+MuF0oUUuMMc/FORd3zk0sdTuMKSeFIKBjMCBXCJ5/Z2OMMcbYmLMMWYDUGGNGwcKJUZIxfxWyPuWY22pZOcYcSJxzr3DOLQW6gD85564qdZuMGQv92YC/rQ64Y+2+BTiH8gHXPRzw5XsLfOU+Hyg1xhhjzK7ZmLN82RR7Y4wJZX61lKyuJjW1luiFR+Hq9nzRivbaCFcen2RzX4GJdRHScZuyYcxe28e3jXOuFjgKECAH/CgIgo3D7p8BfAb4NfB/wDnAl5xzg8C3giCwlG9Tsa5fFrCxzwc11/c5zp/r32jZfEBXBhpSEIvs/s332DZY2+Mf350JuGcDnDHDznHGGGPK3D6cymzMWdksQGqMMUDn7WtY+Z0nCAoBjWuGmDrxCaJLFu7Vc9SmHLVWz82YMeWcOxt4PzABeAxoAN7snLssCILbw91egx/EficIgqfxA9XZ4fa7gAfHvuXG7H+Z3LPBUYDVPf7/7kzAt5cGdA4GtKQdly6Eqt1c2KuOj7xtwVFjjDHjj405K59NsTfGGGDTAx0E4dTDjq0FhvpzJW6RMeOQ2/mfc+4K55wO+3fFiEe1AH8FjguC4CzglcAW4B3OucZwn1nAWuBx57ZX5P89/kLxMfu1T8aUUDLmmFH/bEBzbviOeHAzdIZT5bcMBDy8ZffPMbvBcdr0CJNqHDLBcaxVUzPGGFMJbMxpRrAMUmOMARJT6+mf0kCwrptobYL4qQeVuknGGCAIgmuBa59jlx8EQZAFcM4lgiDY6Jz7NXAxMBDusxlYGARB3jkXAQJgBdALHLz/Wm9M6V28wLF0iyMRhYOb/baaEVmhtfGdHzfciVMcJ06xzFFjjDGVy8acxgKkxhgDTD5tIi7iyA3kaDu6lVhrTambZMz4sw/xl2EDVRcEwVC4+VjgmSAIBsPB6ZPA651zsSAIiunhG4Fu/PQoYypWPOo4on3HbYvaYNugY2U3zGlwHNxiwU9jjDHjzF6e+mzMWfksQGqMMUC8Js70JVNL3Qxjxrl9D9IEQRAAOOfOBV4MHBduLzjn7gWagAXAw+H2XudcG7DGORcvDnqNGQ+cc5w23YKixhhjxrN9Ow/amLNyWQ1SY4wxxlQE59wU4FPAJ4IgWF7cHgTBPcB64ELnXCrcN42f6vSIDVSNMcYYY8yesjFnZbIMUmOMMcYcGPYxoS2c6hQAnwVWAtcNuy8SBEEB+DzwHiDmnLsBuApYDtz2whptjDHGGGPKzj6MO23MWdksQGqMMcaYshYEQeCcez3+6vy/BUHQBeCcaway+LpP1+AL578ZeCvwKPD+4Vf9jTHGGGOM2R0bc1Y2m2JvjDHGmAOD28W/PXmYc63AF8NHXOWcu985lwHuAY4GCIIgEwTBDcDLgiCoCYJgcRAEt45+J4wxxhhjzAHPxpxmBMsgNcYYY0y5ywF1+Kv1jcA3gLuAB8OpTtsFQdA99s0zxhhjjDEVwMacFcwCpMYYY4wpa0EQdGCzYowxxhhjzH5kY87KZgFSY4wxxhwY9nGRJmOMMcYYY/aKjTvNCBYgNcYYY8wBwkaqxhhjjDFmLNi40+zIBUFQ6jaUDRHZDDxT6naUSAuwpdSNMHvEjlX5sGNVXsbj8dqiqmeWuhHGjEcH6Lizkj8HK7lvUNn9q+S+gfWvnFVy32D0+2fjTlNSFiA1e0REVFWl1O0wz8+OVfmwY1Ve7HgZY8a7Sv4crOS+QWX3r5L7Bta/clbJfYPK758Zf6y4rDHGGGOMMcYYY4wxZtyyAKkxxhhjjDHGGGOMMWbcsgCp2VPXlroBZo/ZsSofdqzKix0vY8x4V8mfg5XcN6js/lVy38D6V84quW9Q+f0z44zVIDXGGGOMMcYYY4wxxoxblkFqjDHGGGOMMcYYY4wZtyxAaowZVSLiSt0GY4wxxhhjjDHGmD1lAVJjzKgRkbiqWt2OEhOR6SISLXU7zM5E5FgRObHU7TDGGGOMMcYY8yyrQWr2izCLsA54NXCLqq4ocZPMfiYis4FPADep6o0i4ixYOvZE5ErgTOBKVV1Z4uaYUPiZGAG+B6wGPquq20rbKmOM2XMiUgtcCtwH3K+qvZV0rg/7dyWwFLhbVTeKSERVCyVu2gs2To5dpfevkl+bFdk3qOz+VXLfzPhlGaRm1A0bkGSAy4FjRCRe4maZ/UREYuGPq4AVwGkAlTIoLQci4oZljH4bSAGHWRbpgUNVA1XNA7cADcCRJW6SMcbsMRE5G1gGnAC8AbgOKudcLyKnAvcDhwIvBW4CqIQv+iKyhMo+dpXev9Op3NdmxfYNKv5zpWL7ZsY3C5CaUVcckKjqIPA34FhgckkbZfYbVc2FP74McMBMETkWrB7pWCkG30TkeOA9QA3w70BjaVtmikRkkYjcAPwb/iLCCSJSVeJmGWPMbolIffh/FD8z4S2q+u/A24BF4YyFsj3Xi0jbsJtHA19Q1YtV9UogJiIfKOO+TReRmIhE8MGLSjt2M0RkSnizEvs3Lwz8AhxFZb02DxGRq8Obi6mgvgGIyOQwsxIq73OledjNijt2xoAFSM0oGPlBKCIREfkvEfk70Ay8FlhsH5jlL8xUjIzYNk1EbgbeBGSBOfggUMVcvT/Q7CozVET+HbgeeAqfRXo2flBtxtBujo0DPgY8rqpnA7/BH5sjxrh5xhjzvETkRBG5EfixiLwFf/FzNjANQFW7gf8C3hXeLqtzvYi8SER+DNwkIl8SkVbgYKBp2G7/jT+PTi9FG18IEbkIeBo4PczmOpwwUaECjt1xIvIT4GdAe7h5ETAJKqJ/c0XkN8CPgFnh5hPwZcuKyvK1GR67HwO/B64WkRQgQP2w3cqyb7D9c+UnwO+AG0VkJjCPCvhcCft2I/CbMAhaDxxCBfTNmJEsQGr2WRgI3VV9n1nAi4GrVPXVwC+BJcDEsW6jGT1hTZlAVQsjgqRzAaeqL1fVjwMfBmaLyFHh4ywwPkqKf8twqjYiMjH8PwacDnxYVb+nqtfiB9dLRKRpd89nRt+wY3O6iBQHiYcCaeD/wtsfA3qAo0UkMfatNMaYnYXjuo8CX8FPl/wScDL+M+vH+LEdAKr6M3zG0HnhY8viXC8iHwG+gC93cj4wBfgA8HPgnOJ+qnozEAVODR9XFv0LTQI2A+eG44Nf4I8jUNbH7mv4vtyiqqKq94Z33UQF9C90MfCIqh6pql8Ot/0MeGVxh3J7bYpIUkS+C3wVf4H4LcBtwFB4+7zivuXWtyIRmQR8CLhNVRcBG4GP4F+vZf25En7X+CA+sH0JMAP4Dj4Zo2xfl8bsjgVIzT5T1YKqBiIyX0TeOCwYcCaQVdUHwtvvAw4DFpakoWafDZ9+FgZG20TkU8DPReRiEUniB+Krhu17FxDHT7kvu6v3B6JiQLr4txSRt4rIXcBnReSisMxBGz4QV/QT4AL8FV6znxQzRof9f6GIKP4LwDtF5B342ryLgCSAqnbivxhcgP9sNMaYkguzDf8KvEpVf6SqfwLuABLAeiApIicNe8j3gDPCx5bLuf7bwItU9TpV3QBswH/xfxDoFpFzh+37U8LgTTn0L5zlkwRagbPw4zDBj8tSIvLiYbuX47F7CFihqt8CEJHDwvHRfUBcRE4Ztm/Z9S8MRC3EB/ARkZeKyBzgbqCzXF+bqpoBPh0GtX8AKP67Yh3wR2BoWDkBKKO+DSPANFUtXghfj7/o8ijQVa7HLnQ80Kqq31XVJ4EvA+fig6Eby7xvxuzEAqRmj4RZBVeKyAnDtk0QkW8C38Wn039aRM7Hn+xOLe6nqmvwH6KXiciEsW252RciUhcGdl40bNtCfAbJOvxV0v/C13p6BD/FYgGAqq4Ib58jIgeNcdMrRngMiqUKCsO2vxeYD5yC/+L6YRF5Of7K/JtEpDrctRv/GX+qiNSMaeMr3PAM6rD2ayL8fyL+PfMK4NX4aY3nhV8O/gB8XnxduBSQAzYBtTv/BmOMKZl/qupjYaAN/EW2jfiMy4eBq4ftWwv8eYzb94Ko6qrwgu+JIvIv/Jf5V+Izo74B/M+w3WPAzSVo5j4JZ/lk8CVcHsFP9T0Xf65ZDbxj2O7leOyuBVpE5Nsi8k98hvP/4QM4f8bXYC8qx/6tx48hThSR7+Ezmz+Dn5l1Ez6Tu6jcXpuPwfYZTwl80Pew8CLFTcAnh+1eVn0DUNVfA3Uicp2IrAD+A3gVPov055TxscNfPEoVZwbiywY8ArwOP1utrI+dMSO5ILDgvnluIhJX1ayInIgPyETC2yfjT25fEZFXAF8DvqmqHxeRvwGP41PyF+PrUz4BfFxVN5emJ+b5hNPoC+HPMfwAs0ZVV4vI6/A1ZX+L/wLRDnxUVW8RkU/jB+S3AsfhV7T/mar+vQTdqBgishR/pXYe/v3zfXxw+n781epT8VkSX1fVTFi3qg9/VT6Cv5J7o6r2lKD544KINOKzV47EF+O/FB/8nA/8CviSqnaHGdYfxl9ImIi/sHSNHRtjzIEqLAHyW+CTqvo38QuP3IlfgHMWfubCpar6YAmbuU9EZD6QVtX7RWQu/rw6Fb8Cehe+Zudk4DJVvbN0Ld07YRDjYlV9p4iciR8HdOLPTV+kzI+diFyFr3P4XuAxfM37j+DHQz8D7gFmUr79+zw+8/ejqnpjOHX7FnzA7dPAVsr0tVkUZsXeAFykqk+F224G1uDfg2XZt7Ck1XvxJRKuD5OC/oKfLfQ/lPGxE5Ev4GdCBfjPx//Fnxsm4b+bbMaXKym7vhkzUqzUDTAHrmKwTFWz4abH8HWpfoefjnQ+vtbkBfgPxgtV9Y5w30uBq8J91wMfHFYryByghgVHiwXhX44P9vwn0AK8Gz9l6RpV/VW4bw0+EH4mfkrXz1X1+2Pf+vImItFh9SuTYRbIRnyA9Av4Lzn9+GPyEuAa4OqwzEWNiMR5dsq2AN9S1cHw+XZVK9jsIxFpxy8+9wtVfSIsdzAf6MBnf3xeVS8etv9LgNtV9a0isgh4rHhsjDHmAHY8kFDVv4W38/gLQS8DHgwz+srSsIy2iKquEJFl+MVwLsR/jk9T1etL2cZ9lMHPHLkJv/DUM/is4FtE5DT8Rey2Mj52XwW+EY6REJE/Am/Al7A5C3/syrl/NwHvBFYCqOo6EVkFHIP/3nU85fvaBCAcNxXLQj0Vbv53/MKV08u4b134WrjXAKjqhvDYHYvPUj+B8j1278bXHj1IVf8UJgf8Az9D9CJ8kkA5HztjtrMMUrODsKCyGzGl99/w0zxeG/6/VlXfHWYUfgFYpKqrwn1n4Vc6vTUM3ExS1XVj3Q+zZ4ZnjIa3z8Ifv5fhr8jPwQ/I3gSchp86/EVVvTvc/z1ATlW/MMZNr1hhJsvj+EHHK4AfAItVdWl4/+fxC2NdpKq9IvJK4DJ8hs8dI54rAgQWHN03u3h/NKhqp4hcBnwT+H/4jI6PAb9R1T+KyK3AvfgM0cn4VT1XAO9X1a1j3QdjjNlbxc++sKRLAz4L6kPAA/gFOCvqnCIib8BPRb9EVTtK3Z4XQkTm4bPV7lLVz4a3bweOKo7VK4mIvBN/UfjiSnldisgP8RcjrsaXU7ocuDys/1j2RKQKn4H4Z1W9odTtGS3hmPtHQL+qvl5EXg1cAbxRVR8tbetGl4h8EJioqm8pdVuMGW0WIDXbjZheXYef1nsRvmD4+1RVReQYfD2cq/FBgGX4aaR/xF+5PQf4FHD98MCCObCJyGvx04QfxE9RukhVcyKyAH/V8D7gW/jj/hr8qpMn469wf0BVl5eg2WVteMZoePtl+Jqu9fgvoX9Q1d+KyP8DjlHVE8L96vAZFM1AIzCIn2L/sxHPb1mj+2jksQm3nQd8HL+Kcw6fRb8FX//sA8APVfXrIrIYn019ND4L+6sjj40xxhzoRCSNnzmUxk/LvkZVbyttq0ZPeC59O37c+gz+4u/tpW3VC7erc7+IHF5uU82fSzhz6Ur8Bfz1+PJd95S2VaMnfG1ejp+xNYAfR5RVPdXnIyKPAm9Xv/J5xRCRQ/HJQ034kgFfUdW/lLZVoyNMoroMeDN+xtS7KulzxZgiC5CaHYT1pf4DP/D4DnAQcJqqHjxsnx8Cj6vqh8NaR6cAJwLLgc+q6paxb7l5PiOzCcPbS/DHu0ZVzxGRzwBnqOoR4lfljuIXm3kZ8GZV3RIGyY8AbrPA6N4b+eVFRGbgM3UvwGclPoYv6N6gqovFL7rUjZ/W8pSITFfVZ8JBWNJKV4wOEakJM3KHXyh6JT4L9CequinM3g3wtXanATX4mlIfB+5X1XOHPV+bqm4a844YY8woCOuQ/zfwK1W9v9Tt2R9E5PX4UgH3lbotoy0cwxUq8UJp+Nq8AvhXJR67IhFpUtVtpW7HaBqWnT5B/QJNFScM4DeoX6S4oojIKcDm4qw2YyqRBUjHqd1kSM0Gfoi/4vUWVd0Y1s57O/A/xem7IvJS/GIj71TVf+3u+UxpFQNxu5tmHQbYPgs0qupx4bZGfFbc3OJUHhE5Ej/d/g+q+vWx7EOl2E1GxxL8iqu34euMZvG1z96KX3n1cvx0v1+KyP/iazUdhF8I7cMjnsvef/to2Betl6jqeeEV8rn4YHUXsA1ff/ca4K/Au/BTMX+LXyBrK/AQPgPpPLVF6IwxxhhjjDGm7ERK3QAzdkSkKQyAMWwxmCNFZF4YwHkSWIqvN1V8bSwHHsFPYyF87J/wAYG+8DmcBWcOLCKSAhLgF14KA6VzReSLIvKfItIcXv37BRAXkdZw3w78YkAfHfZ0j4a3rxvbXpQ/EYmEV8uHZ4zWhDWzvgZ8TFU/FNamnIVfhfUcVX0nvmbYB8OHXY0PUh8/MjgKz76fzZ4TERd+duXwq95OFpFDw2P1UmC5qi7BL/7wM+DDqtqHr+02iM+sWhy+Zy7CL1JnwVFjjDHGGGOMKUMWIB0nwkLRG4Dzwtunisgf8EGajwCfCHf9EVAAJgKEBd3vBI4Mp9MTbn+Tqj4c/mxpyAcQEbkYv9r5leHtOhH5Dj7A+TA+C/EzIjIBn734KH71yKL/Ai4SkSYAVR1Q1XtVdWgMu1ERwuB0QUQmisi5IhJT1V58TVcHVA/bfQFwpqquFL/S+XJggYhMVdV+Vb1NVZ8Og66uBN2pKKoahBcO2oBDgHXA68K7a4GhMPsa4BagR0ROCgOq/w38HV+CAlX9RyVOpTLGGGOMMcaY8cICpOPHXGAVMF1ETgAW4euFvgg/pfoNInK0qt6Kn1Z6SliPFHxW6c/xU03NgS+DD4afKyJpVe0GbsJnxT2Brxd7GPCqMGv4X8DJYc0cwm2nqOo2C8TtnWEBteLtmSLyTeAP+Czs/xeWLLgT+Ak+YxQAVf0RsFxE/gb8Ej+Fu05VVw97PlfMCN7/vakcIpIQkdNE5NMiMj/c5kTkTcBd+M/HFPBSEWkGNgJDwCJVzeIXKekBngQIC+6fWkmrrxpjjDHGGGPMeGYB0gokIseKyD9F5MRhm9fiv/DXAjPwmaN1IvIgPqB2P89mT/0En1E4G0BVH1fVr6nq02PTA7OnwmnzPwlrxRZNBm7AB0nfFG67GfgUvn7iVfgpwyeGwaDb8ZmMLyo+gar+LfzfAnF7oBhILi7uM8yLgYdUdRE+SPoK/PtsCPgLMEVEZNj+S4D3quoMVb1VVXNhjUzC57fjsW/OBj6Jnx6/Cbb/Lc8BLlPVDwDvBh4A3gjcCMSAL4Wr138FHzTdvujSLo61McYYY4wxxpgyZQHSyhQBjgMuF5FLw22P4bMIU8BCfPDzNOCDqvpufFD0NSJypKr+FPgBYbaUOaC9BB/8+UQ4tR78VOFF+AVk/i3cVoVf5OddqvpXfJ3ZQ/F1Ex8ELlXVm8ey4ZWkGLgMp9F/YFjQ8/vAzSJyM3AJ8C386ufHAncAncDrhz3PpuELn4XbcmPWkQoRljT4nIg0hJt+hS8tcT7wbRE5XUSSwADQHO7zBP5iwSvwZUbejQ+UngV8X1XfGGaTGmOMMcYYY4ypMBYgrUx34mtNLgUuFpFT8VNIu4Bv4zMMXw6cDmwQkcOBg/ELLxWnWX9VVXtK0HazB4ZNff8rcC0+mP06EXkpsAx4HJ/x1iMiFwIB/vhfJSKfAhYDH8AvyISqbhzTDpSx4uI+I7adKyLvBd6On479axE5N1w86WXAY6r6Mvz77yjgtaraBXwHvzr6TmzhpRdE8NnS7xaRg8K/5e/C7XcBZ6hqBtgMTA1LUQwC9fiLR29X1UyYOf8GVf1hifphjDHGGGOMMWYMuCCwGZuVSETOx9eavAM4Bj+Feq6qnioiX8UvAHMkfsr9MfhFR35o2WoHprC2ZbCrKdYi8h6gBVgJTMVnJ96pqh8Qkf8ETlfVs0XkEOD9+AzTz9mK23snDIq6kVOrwyzFVcBtqnpOuO0qfObhpcBb8SvUX4mfvj0Xv0jTtao6MHY9qFwiMhu4APi5qi4XkZfh//a3A4er6qUi8gl8aYPl+FXn347P5n0v8AywGjge+DFwt6quGPueGGOMMWZ/CGvA3wP8S1WPL3V7jDHGHHhiz7+LKVO/Aj6Nn2a9DPgGUKwh+g/gQuDLwApVXV+SFpo9VgzKicjBQI+qrhaRRLiy/D+B1wB9wP/ip9wfKyJxfN3LK0TkLFX9nYhcYnUs946IHKWq94Z/tyBcvOw9+MXN/qGq94nIlwnLGYQr1X9FRK4GGvELnL0dvxjWzcB7LGN31A3hFx47En8c7gU+B1wG/ClcmM4BdfjjcCLwGlX9mIhsxL9/puAzR620iDHGGFN53oCftfMaEVmgqo+WukHGGGMOLJZBWsHCFZoXqeqbRGQOUKOqD4SBs0NV9f4SN9HshohEhmcqishUfK3Ki4D3q+pNw/cF3ofPhnsX0Iqvq3gfkATmq+oDY9j8shfW/3wLPgvxCeBKVd0oIkuAjwG/wJcx+Dp+yvwgvs7vKap6b/gcvwG+qKp/EZE6fPZpV3hfcVEn+wAeJSLSBqwA3qaq3xeRb+EXI8vhF8uKA/+mqnNE5CJ8Zu/rVPXxke83Y4wxxlQOEUkD6/EXSP8T6AjXYCjefwx+THcw8CjwX/jSPKeEtfuLSQqfA07C1zD/M/AOVd0wdj0xxhizP1mAtIKFi5A8BixR1aWlbo95fiISHVl7Mpw+/HHgEECKC8WEWXGX4Venb8RP3/6Hqn5nbFtdWUSkBrgOv1DPZ8KLCil8oO3D+AF0E3A1fjGsV6vqwyLyDeCl+OzS1+AXxlqiqn3DnjuKL5Vgwbj9QESuwNdX/g3+y0uDql4Tlhy5BL8A0yL8avSHA7ewm9IVxhhjjKkMInIJ8G5VPVxETsYvwjhZVbPhuO9p/Jjgk8Ak4Ev4YOkpqvpXEZmIX9/hOuC7+IuunwTagONsXGeMMZXBpthXMFXNiMj1+ECABUjLQDE4KiIXAEcA3wlrKv4ZmA/MA5aGgaC3A59X1RUiUo2f8l29qyCr2SsLgFmquhhARFLhAj6IyIvxwc9HgB+r6muHPe7zwHn44/Z/qvrbkU9sx2W/uw6fNf0q/CJlfwBQ1Z+HWdiHAkNhtodlfBhjjDHjw2XA98Of/wb0A+fgZ5pcDESBy8La8MtE5JPAD4Y9/s3Ag6r63uIGEXkNsA2/AOTd+70Hxhhj9jsLkFa+j1p2VPkQkcOBL+IXXLoD+KqIfBv4E3AcflrPUuAHqnpt8XGq2iciH1fVnrFvdcV5BpghIq/HL3pVF9YdzQO/BF4erkgPQLj4z09V9cFwWn17MThqweqxpar58P1Sh8+6ngDcEN73JXxGiDHGGGPGibDM2An4MlWoaiAiP8AHTX+GT0BYOmLhzLtGPM1RwEki0ruLXzEbC5AaY0xFiJS6AWb/suDogadYf3LY7bSILAxvLgSuV9VL8ZmM04CYqq7Br759iIhMCwOiLpyyDYAFR0eHqm4C3o2vUXkY0A10Ai/BZwrUi8gXRORDInJ3uE9/+PBvAq8IA6qWMVoCqtqnqp/EL0bXGWZXG2OMMWZ8egM+Q3SViOREJIev3X9GOLtkT0TwNUkXjfh3ELDTjCFjjDHlyTJIjRljuwhavxU4F391+zDgbBG5Cj/gWjws8Pl34Hj8gjPfD5/HAnD7gapeD1wPICK1qtojInl8Fu+rgWOBxcDlqvrgsMf9S0RmWrC6dIYtuLREVbtL3R5jjDHGlIaIxIDXAu9n50Dm9/ELoD4GvFZE0sOySI8ese99wH8AzxTXAjDGGFN5LEBqzH42cpq1iLTjF1v6XbjpfnzdSvCZiMuA96jq0+H+S/BXrn8NfJadp/2Y/SgMjk4FZgB/VdXl+GzeYgDVAZHiMbbgaGkVF0qw4Kgxxhgz7p0FtADfVNWtw+8QkR8Db8InJ3wC+KaI/A9+kaYPhLsVkxr+F7gc+ImIfAbYDMzCB03fZWM/Y4ypDBYgNWY/G7bwUl0YtDkJ+KKIvE5VbwVa8SumA/wemAl8TURuwF/1bsYPvgJ8XVIzBkSkFb8q/RJgDr4O7PdH7FPMVrRMXmOMMcaYA8tlwG0jg6OhnwKfxs8OWgJ8HZ+08AjwEXx90kEAVV0nIscDnwL+CKSAVcDNQGb/dsEYY8xYcUFgJSqNGS0iUg+8A/iSqnaG284E3gOsA36lqj8NFwBagh98/QW4T1UnhftPAV6OXxXzVlW9cex7YsJpWZcDCeAam1JljDHGGFP5ROQc4CagTVW3lLo9xhhjxoYFSI0ZZSKyEXiHqv5QRC4BzsFfiZ4CfAu4WlV/FAZOPwd8Gz/F/iOq+lSJmm2eR7ggVsEWPjPGGGOMqRwi8lrgKWA1cCjwVeAhVT2npA0zxhgzpmyKvTEvgIgcC/w38EngHlXNAZ8HLhORm/ErXH4JOB24GH81+s8AqvpHEWnC1xWNA28b+x6Y5xPWGLUV6Y0xxhhjKlM78FFgIrABv2LcaijhAAAgAElEQVT9e0vaImOMMWPOMkiNeQHCekT/AG4FHlTVq0Ukjq9f9Drg48Dh+CDojcMWXmpX1Y3hz4uBbar6ZAm6YIwxxhhjjDHGGDOuWYDUmBcgzC58AL+65RuA24AvAq/CT9G5Hfiwqs4I918IfAi4TlX/WIo2G2OMMcYYY4wxxphnRUrdAGPKWViP8uPAYfiM0T78SvSPAKcAdwPLROTnIvIn4LvAPfiMU2OMMcYYY4wxxhhTYlaD1JgX7ibg08D1qvoxEckAFwEzgDfjs0knA3NU9Tcla6UxxhhjjDHGGGOM2YlNsTdmFIjIG4GjVPWK8PbhwDcAB5yhql2lbJ8xxpjy45xbCBwRBMH3St0WY4wxxhhTmWzM6VmA1JhRICJJ4DHgFar6cLitQVU7S9syY4wx5co59zng34HjgyBYW+r2GGOMMcaYymNjTs9qkBozClQ1A1yPr0Va3GbBUWOMMXvMeRHnXDTc9BCwFV+yBeecK1XbjDHGGGNMZbAx565ZgNSY0fNRVf1BqRthjDHmwBUORmPDBqTF7S7wCkEQ5MP7O4Fe4JCSNNYYY4wxxpQlG3PuPVukaS+87W1vC7761a+WuhnmAKWqpW6CMcaMtrG9evyRn+xc9+cjF5TtFWznXCQIgsLwbeHtwoj9XBAEgXOuBvg48CbgUeBBoJbwar4ZX2zcaYwxZpwp7bjTxpzjfsxpAdK90NlpM6aNMcYYszPnXARf2z1f3DZyoOqciwOnA2cDbcCtwHeDIMiEu5wPvAq4EFgGvBFYhB+4mnHGxp3GGGOMGcnGnPuPTbE3xhhjjHmBitOUirfDKU1nOOfeEt6OAv8BfAJoAZ4ErgB+H96fAC4DfhMEwa+CIHgiCIKrgT8D051zicBW1jTGGGOMGddszLn/WIDUGGOMMeOec+4NzrllzrmmYdtceJV++H6RYT8nh/08wzl3r3Pu/eGmGPAW4Ojwdhvwv8AHgiC4IAiC9wHHAic45y4IgmAIOBi4Z0TTbgOqgWmj0lFjjDHGGFMyNuY8cFmA1BhjjDEVzTnX7Jx7s3OufcR255wrlhtaC8wCGor3FwvYh/vOds7NGHb7TKDTOVcd7j4DOAJ4g3OuNgiCQWAmcHd4/1nACqDWOfdB59wtwP1AHJgT7rMOmDFigNyFv/o/vdjmF/jnMMYYY4wx+4GNOcubBUiNMcYYU+kmA5cCNcM3hoPRXHjzPiABTCne75xrdM7d4JzrA+4EbnTOneeciwVB8Ef8QPJ14e4LgGuAVv4/e/cdZNd1H3j+e254OXbO6AbQyIlkgyApZirapmRaK3lkWx6HsT32eO0N5bE3TdV6t1yuHYe17Kkdp7FsS7bGGtkKJmVF5iQ0CZLIobvROb+cbjz7x20ikCBAgCC6gT6fKhT7vXfvfee9Zr3+vd85v98JAlOAGOeb4zcDW4A/BIaAl4D/AOxauY+V+x4Aei8Y5gagnSDwhRu9gYGiKIqiKIrybqmY8yamEqSKoiiKotwShBAtb7ndL4TwgYyUcr+UcuQtj28SQvy6EOJzBMFkGRi8YDb9dwhKkD4G7AZOAH8MPLLy+JeAHxJCNAMdwDzwp8CDQoj7gLNAdOXYGWAc+J+klJ+QUv4fUsp/lFIe4/ymmZ8nCKj/UgixRwjx08DtwCzw0Ht8exRFURRFUZTrQMWctyaVIFUURVEU5aYihEhd4r5xYOLCfk4EM+EOMCWEeFAI8ZGVxvUIIfoJgsNfIAgW/3cgRVC2pK1c52HgS1LKZ6SU8yvHngH+9cr1vwQkgUeBZYLZ+v+XoITpZ4BFoLpy7AusBKtCiE0rYzCEEJ8C/q+VY14CfhnQV37+98BfEaxE+AN4+y6liqIoiqIoyvtDxZzri3HlQ25NQ0NDzcDfApsAGzgN/NLw8PDiqg5MURRFUZR3JIR4AHhSCHGvlPKFlfuaCALBXuBDQogvrwR1g8AEUCEoLdKBl4EC8JsEM/CPSCknhBD3EJQsHZBSukKIHUANmFt5Dl1KaQkhvgV8diXofQV4liCo/C+AkFJOCyFeAn4POEwQXyClHFlppv9F4O+FEDWC/lNV4EtCCLEy5heEED8GlC7coVRRFEVRFEW5cVTMuf6s5xWkEvh/hoeHtw4PD+8GRoDfXeUxKYqirFuu47M0Z1Orqr/PymU9T9C76eeEEOmV+3YBx4B/Ae4H9q7cnyX4e2+vPNYMmCtN7jcB318JVLWVwPfvgJ0rjy8CdYKdQFm5DsAU0C2l9KSUDvDnBL2afpygHArgHwh2Ab0HOP7mwKWUR4EDBDuL/hPBqoC7pJT/p5RSXnBcXgWqiqIoyo1SsSUjBUnVllc+WFHWDxVzrjPrNkE6PDycGx4efuqCu15iZbcuRVEU5cZybJ8XvlPg4NMFnv1mjtyivdpDUtaolQb3fw98YOUfQBNBYPpXBNUxj67c7wNRKWUBOEIQVKaBMMEs+ptx0Jv/HSZoeN9NsApgBvjoyvP6QojQyu3XLxjPWeAJ4A7g7MqsfBH4feDrnC93evN4S0r511LKP5JSPrlyrKIoiqJcFcuV5OqSC3Id12SpJvlPhyR/e9Tn/3tdUmioJKmigIo516N1W2J/oaGhIY2g/8LXL/HYLwK/CNDR0XGDR6YoirI+LC84VMvBxo6+J5k+a9HUGlrlUSlr2OPAjwCfJQgUS8BdwE8S9Gf6DeC3CXb0fDMQfY2geX0fMEZQxrQDzgXAEMRFBrBRSnlKCPH7wDNCiD8H/ga4jSBA/lWAlVUAPkET/TPA82/Oykspf/P9evGKoijK+jZVlnzhmKThSgazgs9sB01c24bTR5eh7gZJ0YotOZET3NV1PUerKDc1FXOuI+t2Belb/DFBr4g/eesDw8PDfzY8PDw0PDw8lM1mb/zIFEVR1oFYQkdo5wP7REpfxdEoN4EzwFeBT6w0z7cBfSXo/BLQIoS4G+gETgshTCnlDGABgyulRN8BhoQQnwQQQkRYCUKBrUIIQ0r5IvApgr5RXyeYTP2PBKVT55rXSymfklL+ppRy7Ea8eEVRFGV98Mo2ha+PUfjaGG7BOnf/izNBchTgdF4yVb7252iJXv62cnNyfck3R33+5qjP6wtqVfB7oGLOdWTdryAdGhr6PYKGuo8ODw+vu126FEVR1oJUxuC2e1LMTlgk0zr9W1R0rryzlYb2nwf+b+CnCfo2vSKEyEop80KIrwOPAHcDB4EEkCcIcrcKISJSyn8UQnwc+D0hxC8R7Cb6ZYL+Tz2s9H+SUn5VCPFtKWXtxr5KRVEUZb3L/9cz2FNB1aw9VaXt3+0CIG6eP0YIQew9fKvf2SKoORrjJcnmrGBz9tpWoipry7NTkpdng8ToWBHa49ARf3e/W6/qUPjKKO5Sg9gdrSQfWL9LilXMub6s6wTp0NDQ7xD0b/jh4eFh60rHK4qivN88VzI70cAIabR3hxDXWC51M2rvDtPeHV7tYSg3gZUyo6IQ4hvABwEHOMn5uOYfgJ8laJz/BEGVCAQ9nh4kaGbfAH6JoHfUB4CjwFeklL//1udTgaqiKIqyGtylxvmflxtIKRFC8EifwPJguQ77OwQtsauLF3N1Sd2FrkSQYN3fGfxTbh6lvMvRV8tIH7btS9DUal70ePGC7IaUkrIt6Ii/u2tXnprBGi0BUH5ymvDmFKHuxPUa+k1FxZzry7pNkA4NDe0E/hfgFPDC0NAQwNjw8PBjqzowRVHWLSklB58ukF9yABjYGmPbvvUZjCjKFbxZK/ZF4H8EHgZ+V0q5CCCl/JYQ4kdXjjFXdv4E+EOC2frSynEW8N9W/imKoijKmrFQlTzb3ELs1Tk2ZaDz3rZzE+dhQ/DY4LUlNF9fkHz1TLC5045mwae2sq4m5G8Vr79UolIK2lkeeqHII59ouejx/R2CEzlouJKepKA/9e6vLd2LC2uls64LbVXMuY6s2wTp8PDwUUD9JVAUZc1wbHkuOQqwMGOzbd8qDkhR1qg3m9IDTxLs8PkwQRDKSu8nB/hd4JvAsxec990bPFRFURRFuSZfPiVZ3NFNpCXNmCn41R++igzXZbw0GyRHAY4tS0q2IB2GoiV5YVpi6nBvtyBiqK/Ka5ljn09aurY8t7r4Td1Jwa/dDiVb0BoFXXv3v8/EfZ1YZ8t4eYvo3mZCG5LXdew3ExVzri/rNkGqKIqy1pghQTxlUF2ZDc60qI9oRbkcKWVNCPGHBD2fvrdyn7Py33FgfBWHpyiKoijXrLYyZ95oS+Bo4m0JsGuVDcPsShFw3YUnRn1aooKjS5I394FaqMFPbFcJ0rVsy54ER4bLSAlb98axGj7HD1WwLcnmHTGa20PETEHMvPK13iR9Se4Lp7BGSiQf6iJxbyfCOL+vd92RTJaDjbyaouvr/w8Vc64P6tu3oijKGiGE4MCDGSZG6himoG+z2qhIUa5ESjkKjK72OBRFURTlenq4T/D4aFDf+3CfuOrkqJSSetUnFNYwzPPn/sgmQcSAXENybBlO5uCYlIwWJIMrGzTNV6/nK1HeDz0DETp6Qvg+hMIaw88WWZwJMtyv5Bwe/njLRb/3K/F8ycJfn6T01ycAqLw0R9/n7iWyNQtAzZH82RuSQkNiaILP7oAN6XWXJFUx5y1OJUgVRVHWkHBUY3DXu+ygriiKoiiKotyS7ugQbG8GX0I4V2fxP4/hWx6pD/cS3Z697Lm+L3nl2SJLczZmSGP/g2nS2WApYcwUfHyzYKYiOVsMyrR1AZnI+WTXntb373Up149hnl/dadXPl9x7rsR1fQxTf8dzSw2fb48Hq4g3ZuCZKYgcctkpwmyQFtLyaIyWKPdmiBkwXoJCI6g2d33J4aX1lyBVbn0qQaooiqIoiqIoiqIoa0xsZQXg4tfP4swFm1sXvjJK5Lduu6j0+a3yiw5LczYQ9Ko8e7LO3rsurrVuC/n0pQQTJYkmBL+yD0xNYGowkFGJr5vNxu1R3njJxfclPRujRKKXTo4u13y+8XyNwycamAJC21N87YzO9mawdrcxcWqRjoZNLBviiaZ2Th3yESWLO09PU/cTmIMZjEyI1pj6f0S59agEqaIoiqIoiqIoiqKsURfuIi49H87tG3NpZlgDIUBKfAnDy/DUKz5bsvChVo/8F07izNf5kcE01Y9tIh0V666n5M3Or7tYExVkwyG0IUVnb4SmlhCu6xNPBmkeXwbJ7zfVHMkfv+Ryekwy44To9R16pmv4qQSeL9B3NDOVuo0PnhmhgMHrr5WJbMtSOVrgTEPwqLfI6RN1tn1mgDs7VuuVK8r7RyVIFUVRFEVRFEVRFGWNSn2kl/yXR5COT+rDPYh3KJ32PEml6BKJ6ey6I8HkaINpV2MiFkU0JC/PQvboMn3zdQDc00U69haI7mq+kS9HeY+cuRoLf3KYyotz6DGT2IE2Wn9pJ+HWKGE08g3JF45JTuclEQM+3C94qFewUIOGHyRM02FBsa7RKwQ/NihwfEHFgQd2moSO+0Rx8ZfqWGM6+JKk9NgoLTY7Lh1pCyoSkmq/BOXWohKkiqIo78FkSaJr0JVQs+7vVfFbE9TfWMZsj5H91Ca0qPoTpSiKoiiKEtmcpu039lEru4Ril46PXEfy8pMFSnkHw9TY/0Caez6U5ZlJydGJ8ytQHf3i0nxhvnOpvrK2SNen/MwMpe9OYZ0pgifxyjbuXA3rdBGzNUhYPj0pmSpLDi9JpATHk2hC465OQUtap9pqYuRd7unW+OH7E/Q0nU+4W+M+y0ASn0fdAodFmObbEhx4aQ6hC1IDGvz+18H34ZE9cN+OVXo3FOX6U98+FUVRrtHjIz4H54ISp/t7BQ/3qQDzWlljJaovzgc/j5aoPDtL6sO9qzwqRVEURVGU1ef7klefL7E0Z6NpgnSzgaYJ+jZF6egNA7C8YFPKOwC4js/kaINMs8lAOlhF2HChNyXYf3sTtVIRZ7pKZEeW8JYMruNz6MUShWWXju4wu/YnEEJN/q81pe9MUn15AWemhj1ZBV2gGRoirGN2xs4d53gwXZFYLoR0EECuHvS0/Te74Xh3mJQlGChbhHwbiK6cJ3lNjzFrpjDnqrQ3SX7ugwnGlySVD2+mdWuM2F8+ESRHAZ48Ah/YBpr6DqTcGlSCVFEU5RpIKXll/vztg7PwcN/qjedmJ13/srcVRVEURVHWI9eXzM+d33RpfsZierxBZ2+Y3KLDfZkm4kmdaEw/13cUIBrXODgreWIsSLDuahV8aqsGaET+1eC569ue5IvfrzF10qIjLnDtOq2doXOJV2X1eSWb4uPjlL4ziRY3KRgmNT2E252k7/Y0LR/fQHggBQR9Rs8UJEUL8g3YkArK6W9vFzieZKQAsXKD6J8e4oWzNk7EZO//vJ2uvWn++aklDk9rVMMZWjalKBkeJz4/hxEzSHdFWVpw2Tfq0ThRx4hpNN2ZRFfJUeUWohKkiqKsOb4nGT9Tx7ElfZsj77gL42oSQtAUkSwFLZxoVi143pPw5jTR3U3Uj+QwWqMk7u2kcbpA5dlZtLhJ+of60JOh1R6moijKOUNDQ78HfBLoB3YPDw8fucQxOvA54KOABH53eHj4L27kOBVFOU9KiTNbQ4saGNm1nwCcKku+eExSLUM2D9uyQSm9YQSrO6UvsRo+8aROKmuw584kMxMNkimDjVtj/MnrEiklQgiOLsHHN0nCxsUrQ5+dkpzJS1wbztiSVEjgX2ETKOXGKj4+TuNkARHWqby+TLk5hbezlcaD/eRaDVrma1SWG8QPtDFR0rA82NYkqDkSU4d4CLoS8MXjkrNFSfPwIh2v1mjEwtDwefHzkzy65xBNLy2wxUtxtHcrS0YEbbyIWWlgSolxZxNaRFAJt6AnXRwpqfQNkl7tN0dRriOVIFUUZc05+mqFqdEg8zg70eC+jzWhaWuvzOcndwiemgRdwEN9a298NxMhBNlPbiLz2EaEJvDrLvl/GDm/a6uUNF2w2kFRFGUN+CrwR8CzlznmJ4HNwCDQDBwaGhr67vDw8Nn3f3iKolxISkn+yyM0juVBE2R+dIDYnktvTnRkUTJRlgxmBYPZ1YvxnpmS1F2JFtXJdSbQQxbb9pmU8g7Sh+b2EJmm81/pu/sjdPdHzt3OhCW5lcn8uCm41N5O81XJqVAEJyzptRok2sJ09Kz95PF64leD1glme4yIqdPoasXrS4Mm0L57lnLEZ7osmXyhxMsHNnF0CRwfPAntMag0JEdeKXHyDOBKLAQVqZ1LBvm2izWeIxuB50SCqZJPWLg0+5KmCFg1iT9ZofneZvTlKOwbCE5sSa3K+6Eo7xeVIFUUZc0pLDnnfq5VPGzLf9sq0lLBJb/kkG0xSWVW56MsGxE8Nrg+E6O2J5koQSYMLbHr9x6IlUS4b3nnk6OAX3He6ZSLFBrBqt6eJESM9fm7URTlxhgeHn4OYGho6HKH/Tjw58PDwz6wODQ09FXgU8B/fP9HqCjKhbyiHSRHAXxJ9aX5SyZIjy9L/tupIAY5OAc/tyvo3bkaIheEv0ZrhP23R8lGBFbDx6r7JNL6uUUEzmyV8jOzaGGd5Id60OMmjw0KvnMWbB8e6BFoF/QVfXNl6WwVip7Abo0Ri8f46If0NbkwYT1LPNBF/h9G8Bsu6QNtDN7WxfioRVnXyJVdFsuCpbpkulDlX9ogGwn6jnYnoCsO2pMTHC1WYQEcHWYTYXo3xikXJGXdILOzhUhhlIW2Nn4gekk0R+loTdB4tkHcdTEMyGahUzYI9SZwZmsYrRES93eu9lujKNeVSpAqirLmtHWHqJRcADLNJuHIxb1tinmHl75XwPckmi6465EM6ay5GkNdlxxP8ldHJLMViSYEn94K25qvbyBtZMLEbmuhdmgJYWgk7rtyADZZkvzNMYnjSbIRwS/sCZrRK4qirKI+YPyC2xPAO+5ANzQ09IvALwJ0dHS8vyNTlHVGixqIsI60PIB3LLGfrZ4vL5dSMlcV9N6AhXJni5LnpiUJEz7cL4iZgg/3C+ouFC24u0uQjQRxTTiiXRQfS89n+Qun8KtB/OxVHZp/cgvJkODHtlwcC9mWzyvPFSksu7R3hfCMBAc6BY4HUTNoY6qsLZHBDM0/v43lvzyBdbpIvGiz4cMb+cNDsNGPEMnX8DSNZzZ3kK9LQNAeh41peHbMo7lmcFxP0VmbZtvyImY6gnZXH99PtiMmyyzMGHxtw8O0NioUI2mKmQRtLbDjRzrpOLnAE4safxrJkD5m87Nencd+Yxd6Wq0yVm49KkGqKMqas3VPgkyziWNLOnrCb9tFc2nOwfeC4NX3JEtzzqokSH1PMjtpITTo6Amvm9n22SrMVlbefyl5bfH6JEiLj49Te2MZsz1K9sc3k/nEAIn7u9DCGlrsyr/fNxaD5ChAviEZLQh2tb7nYSmKotwww8PDfwb8GcBnP/tZ1QRQUa6Sm7eoPDMDuiD5YDd64nz8oIV1mn9qC5XnZtHiBqkPXXquYmtW8MJ0sDlSxBBsyrz/4264kr87LrFX4hjXh/9uqyAREnykH6Yq0JsMjq2WXVxHksoa52JkafvnkqMAXt56x+caP10/V601P22xcyDEQRFCN4IkrKYypGtS41j+XHVVbaLKK38/RbEa4TUZodVxmG1JMZ5O4TV8dB2WhM7TkzDZ0DFCIeILeaaSCbYvLqLlLAqvTBPNQMtEjhY0Tpba+cGdfdzRHsT6mYjgEx9I8U/dSV59skJytIBZs/mu1Ln3+zN0PDawyu+Iolx/KkGqKMqaFEvoOLaPfolPqWyLcX6XTiGC26vgtRdLLB4ugCZYui3Dnjvf+/KCetXj7Ok6pino3xLDWIMrIDNhMHVxLhnZeh02qLJGilQPLgBgT1SoPDdL+iN9V7WBQmtMEOyBEvQ0VRtnKYqyBkwAG4CDK7ffuqJUUZTrKPeFU7jLDQDcpQYtP7PtosdDvQmaPnP5nubdScG/3QuzVUFvMkgUvd+qZRfbFeeWbxZW8ptzVclfHg4mgEO64KPJBvNHKyAlnRsi7LsriD21qEF0bzP115dBQPxA+zs+14WbjhfzLhmtzMPdUQZ3xelMqR3J16JKyeX4jKRR0umLB+3HEiGH5rLPoiXJp2LsX8jR1hRiNhZhYVFSyUTRsyFMLcRkZ5bOSo3dxXmypRrRWp2C08y+4yOMtmfRhc+OyUVO7+mhPa6xIS14bLNg4kSV0ss1TMvAqNkIIGyAN11Z7bdEUd4XKkGqKMqaM366zrFXywC094S5/QMX74/Y1Bpi/wNpcgsOzW0mTa1Xv7u5lJLaK4t4BZvYbS0YzZErn/SW8/NPTBA9tQzA8kIb3LnjqsdxId+XvPxUgXolKP2qlL1zge9akgoLfnI7vDoPTRG4r+e9f3F422ap/tUvnNrfAa6vMVuV7GwWdCbWXnJZUZR158vALwwNDf0jwSZNPwrct7pDUpRbk/R83Fzj3G13sX7N12qJCVpi12NUlycdj+UvnMY6W+JuGeEH921BxkPc3RXEMKfznJuQtj3JSydsBlaCptnxBtv3JQhHNKzxMiKiE7+3k/gV4toNgzHySy5zUxaNuo/vSaqTdUoJQeeexPv/opWr9vyTRQ4XQmAkma1b9G8IMW+Fue34DPNFh13lAiFb0ler8mxHK9XuJhquT+b4Am0tUU50ZCns6cUwcshTgnw6g9BC9JcKlJIRQvgkFh0+8fJJOtu7mW5I/vh7FcZrYR4WdXa7kItItLjJB806p7UoX3jZpzUGn15Z6awotwKVIFUUZc05faTK5GgD35NUSh47b08Sjl48o93SHqKl/eoTo2+qPDVD+ekZAGqHFmn773ejRa7uIzExU6CEQACZ6eI1j+VNriPPJUcBSjn3Mkevrv60oD99/YKh8KYU7G3l9NEybS0h2u/ruuprCCG4pxuCtvSKoijvr6Ghoc8BPwZ0AN8dGhpaHh4e3jk0NPQE8B+Gh4eHgb8FDgCnV0777eHh4bHVGbGi3NqErhHbF/QvB4jdsfb77NSP5rHHyzRc8PN1Os8ssv+T3exsCWKZrrfkK1vDPp4l0XVBKKxhmAJnoUbub04iVxKpZmvksglSwxQM3Z/m24dtvlqyMAvwYLxBS81/x3OU1eN7kiPTHosN8FuSnC7FeCCjc/iETam9g+54hdzRMt2ehTmaxzMSRIwq9x+dIV1rsGdzmJqW5/UPbGapax9nIym6XxxHVhzsVIS+SpVFPcR0Os6yI5h+Yhpd1Nk8N02xpZfvN3fyYKNAsS9LNBXiGzJJvS1BjyWpu/D0JPzwJhV7K7cGlSBVFGXNWV5waNSDROHSnP2+5LvsyfOlIX7Vxc1ZhLre/UeiEAJ/Q4aZsWB82f6rW4F6KaGwRnN7iNzpMhgaHTvi7/maN4u6C/+1t49im0TXBD/hCjat9qAURVEuY3h4+NeAX7vE/T90wc8e8Ms3clyKsp6lP95PdF8LQheEetb+akgRDrapP5kLyuqrQuMfj/noUzXSaZ2NGyN8ZrvGaEFSOFEmVLDJ51z6t0TYNZRk5FiNyqFFkgWHeDKIY52ZKuxtOfcc1bLLqcNVpISte+LEkwbLdZ//9ZDGtBXG8iSv2yY/betsqkuaoirZtZZouoDWMLNH6zRcyEeiiFmLas1nWTdZjKdw25oxxuYQSMLC53QiwcaRBYQuMW2PbcUyhyTEDHht/0Zq2Rj689PcxTyLIsJSKAKOz9lwlP7iLMV0hPFsC3eNHefvs90cdCNU523qIsJ0KkW8CAueZH+HQKXVlVuJSpAqirLmdPSGadQ9fE+SbTGv226ataqH9CXxpEFkW4ajYxZFobMrIzFbr6HEflMLolFE0yDXf306+G8uFVg8NotuanTdFgbWR5J0vARFK1j54PmSo0uwKaMCdEVRFEVR3j0hBOENyUs+VnMkJ3OQjXBdq2Cu1dRYg9l5MDHJLZTJtSVZHGhmfKTBSLxGSFlQ7TAAACAASURBVAQ7zm/ZHuOlkw5Hj9Yp24LWmIHpGnTMOYwcqyI8k8q8R19IIxTViWzNXvQ8rz5folJ0kRK+PS3w+hLMVSSLdZCmhge4EXB1weuLkof6Vv+9US7WszvJt6ohfATRUpWOFw8zSxPT2VYKRohwLE6HZhBB8t2BXubTcUY6m9i7nGO6KJlojyGEYNmCDWmJ1ZOmsc+n0K5TOGuxGI2RXqggfEk9bCKlJG41qIZDlMM62dE8ubYmmspL7F0co8XwWehvJvLYBu6/Dq22FGWtUAlSRVHWnMG9CV7OCeoe7N4TIhR+e8N435M0Gj6RiBbMrF7B2VM1jr9WBSnZuD3Gcncb374jhbRcznRF+HeaxtUU7AshmJyymS0GY3Nn3ns5vF93aby6SDIdfDRXnpsltq/lCmfdGlqjoGsCb6X3aEdcBVuKoiiKolw72wsSogkTupPwF4cluXoQZzy6SeOOjtWLNQrLDocPlvHrLj+YC3N0czNLqSiDI1V2xAVvtnRcPFrEP5NnuhpjtipYyrvM+D7uXIXUfAhiIYiZ1B4ZINSnEd6c4hkZJTwpubsr2FSzXg2qsuY8jaMVwSZPUrIlCRNcP9gXqjcZ9INPh1X8tRYNpAV3bDSZLEgqIwUqepjRTBJbSCazMV7v3szxgVa2TOeZa0oiDJ18U5xcscJkNM7xTDdbShLX8Zmv+HTFDTA08lKjkIrx0mAv2hafDfMFdBFl59Q0e6em+JMHHkS6LovRCNlSjdsKBWLLNdzeNPcuLfKxbDPpcPrKL0BRbhIqQaooyprz1LLGiGcgbY9v5k1u84Oy6zfZls/L3y9QKbkkUgYHHs5cMol6oZEjFSoni0hPcsaTLO6JooU1fAwKrmCp/vY+T5fj+z61ootmuQigkrOv8dWeJ0IaWkSnUnbRBYRT195j9WbTEhP81HY4sgztMcH+jtUekaIoiqIoNyvPl3z+iGRy2sIt2NzRq5PTouceP3K6zm5fEupanUqdRs0HKSlOVDjsm2jLdbYsl9mUSnOgJUK9As5SA/H8KI2ZIjtsg5f2badsRtk+tUh2IYe+EKFkRghtydC8I03LfVn+8xFWksCSxbrgk1sEA1tjnDlapeIJ4lkTgO6EIJa28KoeWkuYbs8lPFyg+nSB/KOdZPdkL/8ClBtKSsmJRThxvM62JUncimJ6GjNNYcrZKMKHhOuzfTbH4PgCUcfFD+uYQmDqgthUhYmpMpkotOsekZ4Y2+7J8KQwmevSMcJhmqs1Wqt13LrHodYBkssaW8eLHLqnG9MRtNgWiekF/LBByYGzRXhi1OfRAUlKJdaVW4RKkCqKsuac/vYcg4+PIDyfhT2dVO7aQjp2/g/v9FiDSilYsVkpuUyfbTCw9fJbjeZeWGT5bA0Aa75Od2+Ew8NlkJJM2qDpzjZAf9dj1DQNf7FOrRrcziw1Ln/CO5iZaDA50iCe1Nm2N8GhD2xm+lvTyLDB0Af6aL6mq14dr2hR+NpZ/KpD4v4uojubrnhOYdlhaqxBLKHTvyWKpr33wGggIxhQZfWKoiiKorxHBQsmZ2yKr+c4LiIMjzt09UJ/fwTrdInE5DxL36uQfKCL5EPdF50rpeTU4SpLcw6ZFpPt++KXjHPqjmS5EVTBhI3zj7tLdcpPzYAuSD3Sg36JCe+WDpNkxiB3eBGpZ6iZOvlohG0G3P1IlsVZm8a/LGPPFNE8j/apMj8z/gPGWtI0F2skXYe2eZ+OrjhmxqW1x8MVTeTq5ztCzqy02x/cFecwYWbnoVwXmHXYWirxs4dHqSOYnI9TLXl0f/0E0peMvjbL7j84QKh37fdwXQ+klHxnHNyay+Z8hZQFNcOk07bw4ik29WjsKRRIHRsjvFxmKRZB83xMDzIaULKoS8FyNIJRr3O7XSTZJUi5ISpVwHbpWrYwPYeXBrponylgmQbf3LOZLTM5XE1jqj2D49t8I7yFDxyfQq86HN/Ygp1K8r0JyWODKn5Xbg0qQaooyprT8c3TFFZ2c+9+eQJR2gCx87P+ocjFq0WvtHoUQLNdzJV6Jd312Tcyg3BcyuhsW2yg51LQ+e5XEfi+T8OWOH7w3PW6d4Uz3q5Scnnj5TLSl+QWwBWCl7U4fGwLAFZRcNtVX/XqFR8fxxotAVD4x1HCA0m0mPmOxzfqHsNfnkY7ncePh3A/2ceWvZfu93U1nhj1eX0R2mPw41sF8dDVBVvS8yl9ZwpntkZ0VxPx/W3veUyKoiiKotx8kiGIVhqcwKAiNDqkQ5P06E4Keqdm2OPXkRLGnlkk2Zmla0OEYsHhtRfLnJxycFxJMRNl5KzP5iWbX703RMsFk/XLdcl/OSyZKvs4dZ+f2ybp0Hw8D0LfOIUsBZVFXs6i5ee3v218hqkxtCPEaEwnYXlINDTp0xvxCUc0Ms0Gz1lhFmNNtC4WyJZs2l2L9qUSvhCYYQ1bhig0dOpVA+90lexkg56oyVQ9GOeOZrBGS1TzNkdKKcIRncEItMUEnz47yxyCvzdbqNUgN1nloZpGW7mKXVuicqZEk0qQrglCCEK6pKprZBIGfkXnULaDciZCqCmCv9BguuhyaEMXe8sT6FKCrlEOmZgJnbOhJDVTp71ep6FpHHJCbKlCvQqpkKRcl1i+AFtSjWugCVxNkIvGWNwafP+ydI1pPUIo7vHyQA9hKQkldKplD6flyt/DFOVmoRKkiqKsOaGyQ2s5CCw1AW714v6eXRvClAsxluZtWtpDdG0IX/GazXGoHquAhOzWJKH2GDvOLgArpe3XUM5esyWGZSMkVMNX/3FqNXzkSs9NAM/yiRiChhvcl7ryy7ou/AuSu9KT+LaPdpkFuZXZOuZTEwg3WKVQbjJg79b3NIaRguQHs8HrnijBc9PwkYGrS5BWX5yn+tI8APZ4GbMjplY/KIqiKMo6FNIFPzdk8p9OF9F96MIh3pLi/h6NpozEXYazVY1ZI0Ts9QoTI3WKNckTpzwqBQ/T9hiTCbqTglxN8v0Jyae3nY9Lhud8npuWnJlxidcdRo+6/EiozqYOnabXS/QOBJt/url3rjAyYgat3RHS05KUZ6NHBAObggBs5FiVqUiUencGu+yQYglTBGX5uvQRG5IcC2WYacnizMLsQoXt09+lLxlj9y/czdOjcObPxom/coKerGCgYwOnfnQXp0qC03mJYzSTcpawTIEWN7BcyWRTgnStAbZPzghx5Xoi5Ub59FbBVFlnTE+T6QtxvGJihQxEQufsnGTESBLpCFGRfewbm6MQNji0tZuHzDqm0IhaPqbjIVwbf7HK4e4EI6UoW70awvZxdYPx5jRtuTJLiSiFWJhUrcFIVzNS03AFOD6UNR0jESEjXTqFQ6Rs81Df+mkJptz6VIJUUZQ152w2TXc1h+77TLWmqUn/oiBNCMG2fVeX+Ooem0cuV/GloGusQvShPTzXiFIou9xzZxI9/s4rJi9FCIFseHhCBwF65eo3aco2m0TjOhNn6kTjOgcGI3xGlzzxQoVoVOMTm29Mci/5UDe5L51G2j7xu9oxMpfPzMakT1gH2wUEZIyrXz37VlKu/Ku7iLCOL6++VMcr2Ze9rSiKoijK+mB7Etka49c/08zX3nBYiKfYPRBiaxN4P7WFytMz1Ec8Im1B9VCt4nFiGcq2xA3puLbE8kE3Bamsgf6WRXLj35whctIhHE2w2Jwg6blMlCQbO6HSmcL3LDRdED/Q/o5jNJI6H/AnOF1Ns2jE6GiNs3lnCoCDC3D8lIWMJdC3hOifWyYxXcAHEAJR8ShvSQZLZR2PprPHmC2GcLUcL/zBQU53buPHv3EIO1fkZEeK1uVxXrlrMyU9ylQZnreb6WuL0mm6DAwmqE9aRE/41KIhJroy7OpZnd6syqX1pwWfe0RnrqrxtTMG46OSo0tgLztsnC0Rcn2WkmHObmzl5OZ2PE0jLiT/7CYJZ00qJYf48QWSlTrCspker3G0RRCt+MxHwlQjJsVYmGoqguOD4/nk0jF8TcOXEnwBvqQmNDJSogE7ox6ffSB80cpqRbnZqQSpoihrjvDgbG9bkHj0fOyKvOI589MW02MN4imdwZ3xt+1s746Vac4HPUh91+T7UxoH25qhDSaKgl93JDHz3f+B9zwPzfEISx8EaFx5jG9lWz5WwyOVNTAMQX7RYfnxEZpHLYSAheUs6U9vuOrrXq3wxhQdv7EP6fiXLa1/U7QvTv/dTZRGy5hhjc4Pdr7nMWyMe2w+PMMbOWg1JQd+ohW4fF/Zt4oNtVI/ksOvuZhdccKDaldNRVEURVlvKrbkLw9L8g1JMhTj5z8iyETOx3hGNkzmRwfoPVxl5FjQTD6eNGjujBIrVyg0JKUdKQ70m3RldJqigg9uOH9+/Xie6vE8ST9MplJHi+gkEhptvo8QgugjfbTtDCF0gdl+mVjmzBxLhRopIuiujSwavHGkytahNPPJKOh58MDLhpna18OmpSo6Lq4uMCUMTCww25phMRXGRuJVXRxdw1so0QjZgM1sPEmBOL5jMjztUspKlhsCKeG0GaNhOPzwqVlKaUG+N0OxJ03l/g142cj79etR3oOOuKDmCHYnPawlG3epTjwEniZIV20mMzE0BCEpqSAoGGEyvsCMQLxuEa3Z/KC3g6VkDGOuyoRrsBgz0TzAl6QjkPE9JqNhZM4iLD2WdJNMuU7CdrF0jYahE/ZdFmJhUi1Xt8BEUdY6lSBVFGXtMXV8QAqB1ATmFf72Vkouh14oBeXq06BpgsFdF898hzYkcBZqIMHsirNwQcWT5UqKluCtuUHfk9SqHpGojvGW5KmmaWSrdRoi2NgpKnyuVqXs4XsQXumpmptpcGLMYSQSx5Q+kVfLDH76qi97TYSpI8x3t0mVMHXafnE7mYkKRiaE0RK98klXYI+WeWR+ngd9iWaDdkhCT/9VXcNsi9H263vwihZGcwTx1uUeiqIoiqLc0pbrkr94Q3JowWcgDdjwxBgMZmBbM5gTJbzlBpHtWQZ3xYgndeyGT2tniN6qTyPexGgJ9rYJfmK7wLjE5kzfnYKjTVnyDUnGl9wervPRoSS9TVlCYY2egQhm6MoxSNkzKSw0yLvgeT6zMx5PPV6jddJgBJPWSJj2Ro1NZxZYSJoU+9toL9ToWi6jS2iO+JQ9G7sicGIZZL3EYjTO6b6NxCoWJ7b24efrWKEwZ/rbcF2NVLHOomMiHQmGwKnWGNM9HjYtnrqzm+hQG00xjSsUEymraFvS5+tP5Sh5YXoaFroENIFpe7R5LvNGCAeBhUbEgIIlaHgGCzs3EGk4aJ5PPhGltWYRL9VIFSosJ+OIlMH+uM+dmwxmdY3552rM6wZjZYfWQh1PQMTxmUuEOZGMMe0L9p7y+Vfb3/0mt4qy1qkEqaIoa47me1ixGBJByHHJtl0+Q1qvXtzLs1Z5e8l3fH879lQF6UPsjhb2tWuMlSRSSrqTgta3TPC7juQHTxUo5hzCEY0DD2eIJ89/ZAoh6C8XmTaD5GCfsK76daazBtGETr3igRB0DMT485Ym8m4QVPtZn0ev+qo3hhbSiWy+jis0EwZHFn2WKpJISHB32CBzLeMK62htV7fyVFEURVGUW8MXjkkmy5K5KlQWbFrrFqeyEQY3hPj+y1U+9dppIkgqz83S+iu76O6PUC66vPxkgTMVaERMfuGBBNta3znBOZ1Nksk2iJRc/LDBv//ZZtrarj6jaLc1Udi3nfCzszSqEWJ1Cz1e4+BYDC+tUW1O0r+cx5guUo5EGGnN8uSOfn76uSOk6hZHYk2UM3EiYY2QFufJeJyFeJRSIsm9ZolND+xiJl/jSCPMlBYjhWR3o06rZzBbkTRVLEwkk7rg07EKx2yd2kSFwVZB2kgBqnR6LXok0aBUy1MzM+hhH7ssMW2flOsTWixRbEpT0A0cTaJZEmEIhBQ4YZNa2MRDIJBULQ2kpBw2Cds21C1a2002LVe5PWvxpU0xps9K0lhEzfOttaqREK4UFCMhPn9U8ugmedUbqyrKWqUSpIqivO+klFh1n1BYe1vp+6U0dAMhASSeJsidKJNofedeSE2tJqmsSSnvoBuCno2XKAuSksjmDEiJMHV2Nwe7eJYswUCat60QWJixKOYcINhMaWKkwfYL+p5KKZlrS5M+m0MCs1tb3s1bcREzpHHPB7MszdnEEjqhlEF6fwvW6QqaoRG7/cb0IC3mHQ49X8Jq+AzuirNx241PMJ4Nx3kt3UxqcZmFcBTR0sJjN3wUiqIoiqLcrHwpKVjQEoUewydXdtBMgai6lPIajQWbJWHQIx28soMzXyPcn2JqtMFrOXi2HiZWhdmDLv/DfRo9ybfHrK7jExorobsaidYYXXekaW45v4JutCD5yimfiA6f2a5dtj9jttUkfXs/4tsl6q4gVaqx6+Q0x9qb6D4+T0r6NBoeed1AImitWhzdEmf29i5e8Ex8I8RCJkHUgM2+RZsrMQp14p5A2DZ6b4xf+Uwr8X9YZmyxQakuMcIaj6Y9XjzcoFb3KEZNGjY874bZ228QSjhQh/lpm+5+VWa/FrmZCNviPvP1Ks8TRbSZDFZrNJZ9co5Pf7XOeCRKydAxNWi4GmnNpyoFui8J6wJXh6TnUjMNZrJJevJlwpbHsmXihSVHxh1OVXXM7hj1aoj0ZIk2w2faDjGTjuJpGr0JgQBqLsTVPk3KLUIlSBVFue78uoubtzBbI3hoHHymQGHJIZrQOfBghmj88qUYmudjuMEqUCEll9pqp1H3KBVcUhmDSFTnroczFPMO0Zh+yetrUYPwxqDxvQgHGyt1xAUd75B3fbPs/Z1uA5Q0g3JblmCG/dp68ITCGl0bggBUSsm2/hDxpiwAu7tuzGzsideq1KvB+33yjSpdG8JEoje2XKaWsxmLJ6nsSBDSITtjw+0qMFcURVEU5d3RhOCOdslrZ2x6ahZ7Mh7S8zmU97AMn2hUZ7kkQdPoyGo0wiYhKTlTExzMCdx8BWwbo7udmYqkJQrmSvinSUnl2VlOHK0xWYpSwyBRtPlw0kLXgmDS8yW//aLP2WJQ1TRb9fmd+985ntI0wcbbkhwE0o6D7vpoGjxw5CxtpQohT5KWLsJzaeg6tqaRS8f5UvcOzjgG7cU6rVULP2YSbYlj11zG7DCG7zJ4/Dj+V2Z4fnkbsZKEoo8jDKy+BPWFPLuocUoz0EImYdNksr2NTEryZp4rHF2fbYo8T6K/i8UcN1ox5zA7YWFHDL5VClHct4XiXJ39+SrhUoNqwyXVolP0BcWmEEYdkp5Hou5SN3Sa8aiEDSwPsjqU4hGSRhRZqdPSaBANCWotUZKaz4llyWId6hqMlCBhGlh9ae4b8Hh93CBpa9g+aMCjmwTXodOWoqwZ6zZBOjQ09HvAJ4F+YPfw8PCR1R2RotwanIUay391Er/uYrRGsT/YT2EpWIlZr3hMjNTZuufyKyM3np1lpL8LXxN0zy4RN7ouerxadnnxuwUc28cMadz9waD8van14unLUsHFNAXRuE7igU6m/7eXka6k87duQ4jLBz/N7SG27UswN2WRzhr0D779r3/U8RGOBwKE894TikIIPrsDjiwJIjpsb37Pl3yXz/uW26tQUhUOa3iexLN8PFNgXGPvq1LepVx0aW4zicRUTyRFURRFWS9yiw77p5fZ/Mwkbs3lxJEK045Be3cz2zaGSERNnP4mRgoOxyJhGv9cprXTYr4rTkdukfTwNJ6pESkX+afoNv7K08hZgp3N8KHCEqnnZvjDeiuVhkfWbdBeqzLZWGbLb21DixhYnjyXHAU4kbvymF97pcbYxjYGXp8iW6kRTWpsXlwgpxsk5kpky3UqfWl8V1BoT9FcbTCRTFMNGRxvDbGYdmhOaJxOSdLH86QaDg+eeZWewgytBcidXORsxyDbJ4/yRksrT3t7yBVt/q1bIrbkcTDeSXShxp4jC7TuzBJ6qIf2gRgt7Wt3SaA9VcEr2oQH02ih6xPrVcsuB58uUq969AxE2X1n8rpc93qoVT1efrLATEPweCVMPSGo6DonijFMN8QBUeHBpEc0ofOpu1P8XTUOkxZTix5osC3sIOs+7WWbbRnJC4kUourSEfax9zbjVxxm9BAdGY02rcZETeeslKSNOnEZojNucEeHTmubyX7f5/gS6BpszMAv79Ou+J1KUW4m6zZBCnwV+CPg2dUeiKLc7HzLQzZc9HSY2qtL+HUXAHexDlNl4HzwEnoXTeurMYPOmUWklPi6ht52cZA2O2ExfqZOreIRS+j0b4myeefFH2dv/KDE9FgDoQl2709ife4I5ZkGUgKfO0Lyge4rjuNMOMLRTISuGGwGLhyFEIKMdLAtGykECf/6zLSHdEFnXBLSuWEBx7Z9CV57caXEfmdsVVYNCEOQ8GziDQd8jbAZxxopUn52Fj1ukvpYH3ri8qt0l+Zshp8tIn1JKKLxgQ9nb/hKWEVRFEVRbrwjw2UmR+pEvnOWuGNjzddIjRaJ+LBxZpll0UOtPU2qP8lCVeMHizqi6JNcbHByIcLmuRrRsIEnIZuv88pIg2I6hu1JDAFTJ6F3LkIjAp7vYzmSfNQkU6qQ++JJQgNpovta2d5kcCwn0QXc0X75MUspsRo+/W0hRMzA9UIMeBaeDYbdQFYtlpIRvrexl9alCl35KpmlMg0hyPe3gwciJihoAmeuzoANW0oV9s1PQcZE+B6u06BRqzJvJDje1EHH+DKOb/KCEebeSAHv9BSx08tMh0yW5i3u2RZn4KNX3zbqRqm+skjxG2cBMDtjtPz8doTx3uPWM0dr56qppsbqdA9EaGpdGzu0V4ouSxZ8pRThuGVQtQQlHwwBtqfxUjjOgUYV0ZC8OubxwA6f/R1hXpqWvDLisABsSdokQpBviVAZ89gwXULTJF0RGM0mSP7/7L1plBznfa/3vLX1vk7PjlmAwWBficHCTSBFUZIlWTblRbJ9KdvXtrzEsXPjxDcnyXH8IXHOSe79EJ/rxFauj+9VfGXJtmxrsWhJlERSorgNSZDYgcFgNsw+03t37W8+1BDAYAcHJCGinnNwUF1dXf1Wd0/Vr37vf9EkyYjgBZlgyq1RKC7Q4zV4WFtkcPcgni1JjFQpTHt0J+O4CYPORGiMhrz/uGcN0uHh4R8CDA0NvddDCQn5scY6X2H+C2dwmx7ZA63oXavrV7YNJtnYrjB3wSKT1+m7RiTmlSzGDNqWGmi+TykZZXHapm3g0vPVssvCjIVjS+rVIGJw1ZiaPlOjTeo1D1UVnD/dIHKsjLsifEpnq/iuj3IDQXW2KHn+QhAFUDKhPSE43LN6m/6FZaordUoz6p1p9/lPZ32OzEuEEHx8Awx1vPPiI53V+MBP5N/x97kRPeUaPbrDVKtBQvrsmVtk+fki0vEBkL4k/+mNN9zH3AXrYrMu2/RZXnDo6r03DNKlpuRrI5KmCx/sFWxpCUVrSEhISMi9ge9Lzh6vU6+4RKRGZbxK3HFRyxYiHUU6PqWZJlMD3UzNSl7yE8zGFFLSJ1N1WC4I6i1xMvNV0gbIqEY1amB7MF0D1/RINTW6PcjWLeq+xFKgU7fpe/FltOfq+F15hu8/QO7Qdlp0n/3eMr+jmyD7rk7VWaG46KAokLdt2sYWUKWPVzdo7GrDMzTmp5p8ZdcAY7kM2/0ZtHmX7RcW0RTBzMYCvZoLNZeKqqF6kvlohJRhM57toE0rcc7TeK17A6dynaTTGWYSBeLCJldpUp4zWXBtNjdNXo8YSCEwfcFXnq2x6UlJRLs7dUTz6NLFZWemgbtkorevvXa+esXxqnfQJXEdn+Ov1mjUPHo3xm67tmu2Raema5R8QUyR6FFBrQkSsIVAdaHoC5bTMUbrGi3jHnOqwmAOdvfqzC27fKpfY9f2KP/nq5IWq0k+IvFdiVZ3aYtaLHk+5+cFVVQiNZspNUHOszkQM/nZdSZnT3pIxyEnJRuXa8Q25OlIizB6NOR9xz1rkIaEhNwZpr8+wdTxoDv88vwFdvzJfSQf7sSZrhPdlsfoTTEIDO64fpOlK8mWLbJ1EyREHA/7ishBz5WomsD3A0HjuXLV86oWFJd/yziNxlQy+RjeaBkAdzAHyo0v6LZ35WPJ5d08pZQsSJVaBJDQlJdOp76UKG9DMDQdyZFzDRhbQGoKL4oOhjrWbrxKz8cer6EkNfS7tMN7rCPGpylRsSGGT66tk9pJ/+LzfvValWhXk8ld+g4URZDO3DuXuK+fk4xXgr+Dvz8Df3ggiEYOCQkJCQl5v2M2fBambcymh5nK0NoNiZiLU7FRVIVm3CC2MU380Q5OnK1zGh1pSpoKLKajrM+pTO/txo7qtPs20+sLlIRGewRcH7JAf9QnsTlDx/ESS8kIviEQ5SKnNIUtukGsUuXE2DLqfS67Jsfx6k1SJ07BUhE+sveqMderHq88W8b3JNJ2Wd5YQJZM/nlDL6cH2om0ROnrWMY0olhRnZHuAuvny6xfLNNhW2zfn6ez0mQsnWDSUDjuqkhToa7r/Mu+g8RSFiO2TjGWJea7dOoGXXWTmWiMpOHSUW4wvaedecdh/nSdhGlTSsZ4ubeL/Bs+v71HQb8LdYTeGcceqwJBfwE1c2dKAQxuT9CoedSrHt3rI+i6gu/JW2ouezNOH60zPW4CUFp2yeQ1kulb16hGROEnP5TiB9/zON9Q0HVBTxaOLYHlCjK6YN40qEmNqZpkPiboyEssDzYUBD+xyeDTWwIz83885PPH8xp+A3QpaVEkfkrFRVBRwPUgL3xswHTho9EyAz1x3jhSZnhKMlWVgKTVlnxm85o/mpCQu4575+7xbTI0NPQ54HMAHR0d7/FoQkLuPhaKEulK8CW1msB0BZnH1t3wNdWyy+k3a6RzGoPbE1fNPnpCBKnwgK0oaA1/1fO5Fp18q0Gz7hFLqORarjRQIZPXLxZajyVUpnJJmi0ZhJQoLSkeuslxbcnD6R0b5wAAIABJREFUQFZwriRpiQkOdq4eoxCC57o6yBVrSGCxNc39FcmXTklMDz7YCw92356oMvCIvzlKYyUgNivrcP+229rHlUhfcuLPTjN61sRQYegz3bQcalvTPt8JtJYohc9uInF8Gb09Tvy+VrySTfONJYQqSD7UedN9rNsQQyiCStGlfZ1B8h4ySM3LgqhdX+L6gjtUliskJCQkJOSuZnbeYd6B5ZJPiyI5uq2bp1uTbOls5WBxmbKvcfRQP8lqk6VzC6STaSpGFDNq8JFNOj1phZIpyfd38Pq8ZL4B97eCroLIC6q2ht/QyVoWW/IuuazHP8QKqJkMTiPB1JJkUFoocQNFldCwUFiZVh+du+aYaxUX3wvEbiUaIaLrfGd/LyfjKRbzKdBUouvz5NIGF2oaJQGL6Rg9pSoRRXDAr7MQ0Wgt1plt04lpCkpSxai6TMWjLKczTPkGGmAIg6zpsaW2zODZaSxDY64tgzXUS6Y3zomnZrjgaiyk42T6U3z9nOSRHsmutrvPIE1/aB1qQser2MT3taJE74zWi8QUDjySxbZ8XvpeiXPHGyRSGgc/mL1mo9bbwTYvC+SQEtuS19/4OnTmNP6vjyk8NQqKIhnqEPy7V3zemIeIqrGUziBMF6EbjJkq0/PwZAt8ZqtgIBuYo01HognBf/PhOCMjCkbZpjFe54jqMy4NtrSp/HAGFlry7CpOczhqYX50iD87pvCyFUc3K1gemO1xNsQUpuuCXNigKeR9xr1z9/g2GR4e/jzweYAnn3zy9s9mISHvc9TBDIvfn8XxJJmNcdSUwde/OMfynMP6zTE+8LHVnYaspseX/48xSjMmiqZw/0+1cv/HW1dto3seviJQpMRXBFpitTDp3hBls1vDma6gr0/TvWF1EyfdEOQKOrF48Lpcq87UWJVMpYmU0Jyo4kmJdoNmRKoieHK7wHKvXQ/UdT3OtGbYXWsiEZzLp/n2uKTuBKeJp8dhTyskjFsXl6rr8UuNUb7pF4gJn582m8DaDNLGdIMTpyykFDRdOPqdRR65Cw1SAKM3hdF7qSh+7okNpB7uRES1m9YffYvu/ijd/e/QAO9iHusT/O1pcDzJ4R5BXL/7bmpCQkJCQkLeCV6Y8lhatJE+jEuNaEql0GUwEetgaiJJNgJOyWepUqPLrWM7BhE8Nqea/IcPtWN74PiC//imRBESTYGzZTjYAU1X8uIMFP0EZ1qT/O85j8joMsJQWb5vKwsJi+j4LI11KR79tf187YLKtFDYODXBa1KyZV8b18qhyhV0YgmVeskBV/Lsjl5eS2WZSiXRDYXWap0NE4sUFYPBZIzchRIPjM2ScWyino0V0Um0GMzUBUOdCvWGTuLYMp2Ls1yI9aJXJK2GR8OI0KpKdqgmfaNzVBUDgc9UX4EHc4KlhM7OJ3qYG5N4NWiJgy9hvCrZdZty0Z6uY49XMfpSGF1XH7UzU6f63AxKVCW1YnQCMDYPPzoFqRg8vhui148KFapyS5Pmt8LkuSZnjzeIxhR2H0qRSGmcOlLjwphJPKVSr7pcGDPZsOXWsq8adY+5KYtEWqWtM4KUkokRE8v0cByJrgtaOyNkW96eBZOPKfzS9kuPf2ojzNR8ohqsa9dZMg2WypJSHTQH/u5M0PB1Y05QsyX/75uSsiURAn5x0GDshQbllMFoVcNP6qga3NcOi02Fndt6ODTQw1MTAJJpVUcM5HE8iaoH91f6u9+yICTkHSc0SENCQtbE/PEyUxEF6fsUqx76Nxc5f6oJwJEXHTp7o6vS62dPVSlNBs/7rsfZZ5euMkjnCllso4rq+1SjMTKJ1aFw/tkS65tV3AJozSr+2RLsumTEKqrgwCMZzp9uohuCga0JXq04VGLBNKdnStRbTIG/Xg0mRRFsmVpirDULwPbJeRQGLz4vuGkW/9XEIoy0djL46ihSCM4c2s99t7mLK5ExLWg16a7U8kzcHQXnbxWtEE5N3wqDOcEf7gdPCqJ3ad2wkJCQkJCQdwLL9hEFA2H52LqGp2uMN1S08Ro5W5LRIFm2qK5P85hzkiM1h6gi+W8/0kFUE7wVhOhJyYaMoO5IJIL9nfDVkz7urEXa81moq3xxdzd/+pkelqfg2SnJm/070HoHqGiwYcHmJwfj/JfyBqycwisRg6kt6/mpa4zZiChsfjDLf3q6xmJLimokyjbpYes+RQQFz6G92CDnmNSjTWyhcHSwi4FGle1piHQZfKWZJLHJYMaDkSmXvcsmWddk7/I8E6S4EFFpR+GQ7vCZjiZHOyIsVVRcH1TLZWrB4/5d8OVTweR+Qg8a/+xqhY747blf9nSdpb88ybCMcVQz6X9U8Kn9sYuaRHo+S399Br/uIqWk/so80a05on0JEs+9APZKKozrwROHbvxmPzwJZ6ehvw0e2XHdGq83wmx6HHu1BlJiNT1Ovl6ne32EU2/UmZ+x0BYEPQMxjMjVn0O56LAwbZPOa7R1BqWwbMvnxaeLWGagt3fsT+E6klNHagBoumD/4Swt7fodqdspZZBC3/RgvAIpA356o+S/fzaQ/AkDKjacKkp8KXlhWjJf9xktw1wDTk/7/IQlOeIYGJrAc31enlHpTsGOQpAF15UUQHA8m/NQthRcH9al4GCnYDC35sMICbnruGcN0qGhoT8FPgV0AE8PDQ0tDQ8Pb7/Jy0JCQq5g/uQcuhkYnnLJZnGmuer5Rs3D9yTNhk80ppDJqhiKxPYDcZBPXCsw2yLimihSEvFgbqpJ52Dm4rN+zUEoAt9QEEj8mnPVHhIpjR1Dl6IRpaEjVwSvNFRcx0OPvP1ToBCC7qUSvgDhSzqKVT62QfCVM9Bw4bFeQew2o/gajuR7u/cRb+nHi2ik2nNrNkiTLRF6H2rhyL8sEo0rbP+Zrpu/6BaYq0uOzEvyUcFQx9URtiHvProq+PGyv0NCQkJCQtbOo3tjjJ6q0ay4PKxanOzMMelALq0QHbWwpm00XbC+oDHz+AF2zs5xYGeGykA7Pzzt8dK0pGwLthfAdCWHugQ/v1lhcx6eeqGJ6vn4EiK2h9ZwEVqU3rREVyTna1BRInwkYfH3L5mcnYoyWTXYs3kzKQM6bpCt9I0JOBZNIDIOpunTKVwGcoKmJigsWIxFY3Q4FnVVZTEdQ25IkxRZPpMs03ywhdypINrz1IIkk1CoZ1O4lQj77BL7isucHcozl4jxc5sj1J4u01ww0YmAopDwXHzLYVcrPH9B0JOSLJqCmg0fHwi03e1gn68w66s8o6cBqI055DuifHT9ikFq+/j1wAR1ZxrYUzUsW7L47DTrNJNE24omL9Vv/EanpuDpN4Ll8QXIJWHP+lsep9n0+N4/LbE0b+M6Ph09QcMk35fMT9sk0wq5gk6j5hGNKYDEdSTaiqavVVxe+l7pYv+DvQ9m6FgXoVJyL5qjAIuzNsplkRKCoEfBndLLZ4rw3KTE8SAbBceD74wLBrKSqg2WB90p2JqHL56UDM/6vDIbmKcxDaJRheM1nYYPM65CNiloOrAlB90pwWITNmSDCfgT8z49cfjjBxQK8VDvh7y/uWcN0uHh4d8Dfu+9HkdIyI87wrGpaHFcRSXlNNi+TaVyBJoNj3ybwcYdcV74bolK0SEaVzn4aJaPPp7gzWdKpFIqh399w1X7TJsl4s2gKZIUdaZNyZ7Lnld2tPA3LztMNKA3Dr+9o+WqfVzJxGAnynwdISVWPo5Q15YX4nk+f/rxQ/QulPEUwYWWrfzbhOB39r594RDVQB+vszwD4NKeasI1E7NuHbvuMvvSEpmIRHgeE9+ape1zA2vaZ82WfP5Nn9kaRDWJ6Sk8fOOysyEhISEhIT/2WK5kyYSW6PUzTELefda3aPzuZ1s5fc5i4zqDXFEluSBxNYPGqCQVFaSSgm7psLEnSf8HN3F0UfLlVzyeGoOGA/1pyasTHq2eQ3ccyt1RakmVoTbJ6QVJtSZpNW0Oew6eG+HIvMSXoBsKRQt+VNP4YdNArUDThfGK5IO9Cod7rv07qViSb41JTi+Dlsuy3bB5dAN8c0mnuOxS16M0cgrTXVnOJBI4UQ1D+owoafJ7u/m4FCQNWG5KXB82dyjEkzlm2nRGJYzlU1RNjS0J+MZTZdpmYSadQVus4UVUBsZn6U43WZdqp133mDtaImf57Nsc57G+1DXHfCOM3hSWshA8EAI1Y9C8rD66EtOI7Wqh+eYSvuXh5OPMnDeDmpyRBFtzJlpEhYObbvxGleYVjwOz1V1oEtmYQU3duGnTc99cZuRkYMKaDZ9EVqelVWfTzgTVksv0mEmh3aBsuDQbPkdfrjIxYnLosSyKIigvuziWDyLIJluet+lYFyGV0TAiCrYVmKT5NoNYXGFm0kL6klhSJZu/s9PYK8lhFy34qh2kyCsC5hvwq9thV6vgr09IkjqkjSDadFsL9KYFve0JyhMeelNl2Rd0p6AzGdQr3dcuUITgIA1iM3UMAeVsgsK2td2XhITc7dyzBmlISMidoeQlmE+l8YWgYUXJ5aP8yr8pUKt6pHMqs5M2lWIQ4Wk2PCbONUm4LukYpHQPYQXt4uVKVyYhBCc6uxnf2YKUko5KlXhltbh8pazyTE8nlYZkNC7YX1b4QPrG4zy/rRWpR5EI/I4oYo11cxRFsJBJcKElDRJi7tVRrLeLa0u2YTGRDFKc1psmazVIGwsmVtO/mO5fnLbWPM6ZumR41CYxWaGZMHgxkebhdfdORyDPl9ScIJ1JCSNnQ0JCQu4JypbkL49KKpYkExH82k5IR8JrwN1A2ZL85Y9cauMmyusOT3wogZVXma0IuvoN3OVAY/o+5HTJuRdKvDQN54jjekHK+Vxd4puQMgSm6fP/vWgRbY/y6mKUqFNnd73GgXaI2AZjZxoUkjFYgq7eCOVFG1coqLHAnItp8JF+wW/svr7YnKpKFptg+9DwwYmpdCzU8KcdmqMWiu8T0VRqmk6kNUrDhKYEV4cTdTj2suRwD2xvEXQkYNkEO25wRsuz2JTM1oN1TReO1CPsME3aklE2n5sj5dnE8hr3pRwU1+dD5yb53oyLrgq2vjJP7eAWktnbM/OMniR7Pruek6/anIvGybRGeLBL4DqSM0drmA2f3ge6KWyKww9OMvnUDIrSiptNMHV4Px07VVo3piF7E927vQdeOoNcqGDO2JS+XaY+8wpVRUfEdQb/p90k2qLXfXm95l1cjsYV9hxMsWlnEoBsi46qCapll6nzJvZKRGh52cFs+MSTKpWSy9hZE9+XtHdHKHQE33kkqnDosSyzkxaJlEZHT5B6/8DjORpVj3ybfs10/bfLphw82iuYrgf157e2CHa3CV6cCZqL3dcO803Bkhn0VXhtTtJ0oS8daNfNecGhTpUJW2FmXuJZoAtojcPPbwbjxAJf+2KdY2Me8aRCz0CMkRMNBlYM0pIpGSlBexx60uF5MOT9Q2iQhoSErIkxNUXbUhnd81hIpxg726T7gEJuRQRcKQb8oslXnzGpuwaiKKl/cZrC51L886hEFfCpQXilv4+0GRh5ZzraWR9dPZM9WvRZnjaRpstyVGO0GOUDPTcWHaNlON/XhgS6ms01p7gIIbDfikIV0NTWfjrVNIERUVCqHoqAeHLtpmOqO0a61aCyYAPQvesmTvIt4Jse+54+jVYNvqN4rh8OtK95vz8O1G3JXx2TLDYl7QnBr2zntksphISEhIT8+HF0IYj6g8CQO74kuP/OVK0JWSOjiz6VExXwJT4uwy9BansapcUg1R1F1SVSwoFHs5w/1cR1fKI1Sa7qU2jLMFMPGs7EVZ+cGphiNQuKNRmE42V0hK1RiQhaXPA8OLxOIIBlU7B3b4zTy5LSWclkVZIyBJ/ceGNtENWCf50JkBJiFYt52+X1OZVCw0UqAl8TZGIeWioo32SvpE6/PANtcck3zgWTtf/rQwJNEZwr+ZxclJguJCoWStWjkTbwYwZnEgmkBxtycYTUcfrivE6M6UXBiVMWybkar7QWGNFSvPiSzW8e1shFb6JvZovB4DvzwTH1pfjlTo/F52Yxlj1SXgcn37CYPBdEfS7OWnzw/Mto9QYt3S71ZpkLj+9EjaoktuThVnRvIgq/9RHq3xihIkuYZytcGDWRHQkUw+XNb8xx/7/uu+7L996fZnbCwnF8WjsirN+8ugFTV19grjq2vDjuaFwlElXwfcnE2Sbr1kdo1DwSKYV0VuPNlysszASRpBu3J1Z1vU9nNdLZO2+5CCH41CbBE4NBSn1cB00RDGR8XC9oMOtLyUhR8KmN8Npc8FsbyAoyEcEvbFXwpaQnJXlmMthnZxIajiD63CQv/MsSZ4o6tlRwHJ3YrE1Lu8HCjM1rr9b4ailCutMgFlP59GbY0hJq4ZD3B6FBGhISsiZ2jJ6nlM3gqQobZmeZO6EjZS+mGxhHrZ0Gm3YlmZuyyLRoRBWPZVvF9kAgGC3Ci+ckri9xgG+eh5HOHH0LFaKOx/n2DL+YW32q2rhYplDzKQuVTK3JwKIF3DjNfjSVxFQVkDCua1i2R2wNNUgdx1/1+I7IAgVOtaZZrtRBE+RbktykTP1NUXWVx/6HQSZeWCaeN+jcu/aK6q21JhuFRS0W3FBsLJWBe8MgfX0eFpvBDfJcXfLmouDgnWmmGhISEhJyF5OJXPH4xpm8Ie8iBUOiSMlbymyx6jFbDEo1LXem+dePpunJq3iuZORYkF69KSdQ4tDWLWiPSx5ap1Cb8PjeWZ9YVOHhPRG+NgnTQCVuMKMZJKo2RUXjYHcUVRE82ntJ/fWmg8jRsi3Y3yFoT9xYGfakBB/tF5xelugCBl2Hf1rQuKBHIOFSqNuoviTWnSARh7oDy02oOjBdD9Kpe1JQsSVfOiX4xa2S708KIr5HfKpBrmKiKGAtmhQHMxTiBqmKT8umfhamGtQLEX7Y38byKz77d3fQ+ME0c6UgSrJ4pMqrBfjQvhtEc37vKDx3PFh+YAt8OCiGVf7qGO7xZVzAPlOmufNSDabKZI2jLyxgt8RYnzPoLSjU2yMkMyrK7cQE6BquFgVVRUkZONJCcyUyrlDXbhz52r8pzpO/302t4tLaYaBq1w6w2LY3STypYps+vRtjqJpASom6EsxgRBSSGY3Xni8z/FyZ0vJKKbFHsjz8E/lV9UfvFM9M+Lw+H0R5fmpQENeDdPj0ZeemrpQgYQRRpRBsu71V4VOD8OaUy8QZmw2Gx3R7hK6+KL+8Q+HMmTpT8y6KEWWgz8B6pYLtBeOP4eMogmRGY8/9aV55psxYA8oNH/OCTe9AlLOl0CANef8QGqQhISFrIuo6aI4LmoaQEjFX40/+scLUss/ufp1f/2CCga1xBrYGM7RzUya2oWI1JEIBUYit6vauCli3WOWR4+Oovk+62YZ/sB24dPXfE3d5wi0xrkTo8y32xLM3HaenXor4NHyJtsbaYZomAjG+EomqyGs1m7o9TBdGLIWRZApNgWj1zogNPa4z8NidMzAzXTH29enMLLnENNi6K3nH9n23E79Cd8fDq2hIyD3N0NDQJuA/E8zSLQGfHR4ePnvFNn8M/A6B1wLw/PDw8H/1bo4zZO3sbBWULIXzZclAVrCtEBoCdws9rRofHopx/FSTdFRBGUwwsiLLhBAIQ0FRBIoh6N8cZ+x0A00V/NyjCbp6L3PmepN88qFLD3MZyY8u+MzUBH53mpQGQhVMmApXBg9riuCR3lv/Teiq4Lf2KIwUA/N99nSE/3LOJqZ4zCVjLMYM1qsOuzfG2FmAN46ZPN8QGIoKSY0lE0oWbMoH0aVjZXBKNn0TFTpLJi2apNAb5eiipDvh074xxuGMTls0yZdmNIbnoOmAZUIkkyJ9fy8jYx4JFRQkzx8zb2yQvnRm9fKKQWpPX2qy5C6Z9PQYLC+4NM+VKZ6uUC1qJBeXOTnYwvqHeiktOZSWHJbmHIbWCxrPTqNEVbKf7EcrxADwKjbN48uo2QixrcFEf+JAG+bJIkpCIxexsGxJbW8vvbtvnimVTGsk0zcWcIoq2LBldXSpEIK9D6Q5eaSGogi2DyX5zj8sUi66+F7QmHZ60sK2fKKxO1t6arIieWZSIm2f5QsOT/san9xx9SxNyhD8q60wPAe5KHxgXfCb/IWtgsaJOv2GT7/ucWzYprM3wrGTTeT5OhlH4hYtEpuyGP0peiaXWLY9yuis353gY59uI55Q8TxJTgFNSHw/+CPrSYXnwpD3D+GtXUhIyJq4UChgRyJIBI6hcUxt4cWzEhOF40s2e9oVDu28JDAUTdDi2zSqNooqyMXiPDwo+Nq5IMX+kwOCZ0dnUP0gDmDn+Dzd6mpzL763hfQ3l+mYXCbdEyO+9+ZNmkyhYKkqAomQPmLtfiYA4g4Yo2+hKbDQFLh+UHC/vPZyoUBQ33WhEZh7SWPtIkZN6Gz4rS10vrGEmo8Q39d6B0b548GeNphvCMbKMJCFHYX3ekQhISHvMX8O/Nnw8PBfDw0N/SvgL4APXmO7LwwPD/937+7QQu40D68TPLwuNAPuRh49nObgUAJNE5Q9wdxxSdWW7GwV9KSgUfeYHjdJZzUOf6IFVRVomuDUlIM0VDYVBOoVUX/3dwnu71I5syz54slAlwoh6LyBbzhSlDh+UCPyyv1dSVQT7FiRUOeXbVpVj4ZtUxIqKddhqT3BF09CQfFoM30aQqdoQ1b36E2pxDToSwseXgcDOYFcsMCT6HEVtWrBgskDeZ22Po/2Vo/udoNsBHZ5kh9c8ImosKcVLE9wqF9jbMKkJDU6pYMVjeJ4El29zjHkkzBTXFm+VAorti1H7flZACIDaVoGEqQ7okz/37OcMFym+jeTqJU5//hO1LY4rJSAalRcpv92iqQIPufS18cp/OoWfNNl8T+exKsE23kf6SF5fwd6e5zW39tJ8Tf+gU3+eZTxJdynzpD59GeBOztxbzY8ykWXdFajpd3goY/kLz7X1mkwclzguZJIVCEeV+5ordG3cHyQlod1tIx0fM7PCoodOXKFqyNm+zKCvszq701TBJtjPk3fW7X+1KTLQlPScMCTki8fcXEO9vJYZ4J1JRtjewuZjks1XbfsTnDySJ2PpGz0jSm29StsDyeLQt5HhAZpSEjImpgqtBFzbVTfp6inmPNUpoWOBEponJ2xVxmk6mKd7PFZ/EgMzfeI/bBB6ef7aLoCRUDFBnmZoHQVQeyKi/wPTzi8lM7DNsmoELSccDi898ans4amgpRIwFQVTE+SXOsZUIIUl5bvBDsLsNgUqAIGc2sXHFJK/v6M5PiiRFMEP7vpzqTB6O1x9A/Hb77he4xt+ZSXXVIZlWh87bP5ihB8dH0oBENCQmBoaKgNuA94fGXV3wD/YWhoqHV4eHjhvRtZSMi9STwRXOdbgd/a6jE5bpFLaLiOzovfLWE1A3NocGeCng0xvvC3y5yadhERha0PZvnsXu2aNeo35QU/t1lhvBJED19pPr3F0+M+P5ySF1/zi1tvrhdsy+fssTrjZxrsN11yWpRq06erIJAvnSFuu3xr7wALEY1tKZcRoaLrGq0JGMzCE4OC3W2BIfeJbSpv2IJ4VmVhFGJxheKcTf2lCp8niZeX3Lde4w+GBHN1mC6BqsFPbhRkIlEi50ApeZQiEQobItc3RwH58w/ifullUAX6zw5dXJ9+vAejP4W0faJbshe/l5b1cQaWmhxfVCh3d7D3/g46602qKwaprguE68OK3ydNFwB30bxojgIUT5Z5sxahWnaJJwRbj5xBW5xD0UCbnoEv/RD+8KdBubZJubzg0Kx7tHUZ6MbNjcx61eOFp4s4to+mB42YUplLNxAPPJ6jXvOYGGnS2mHw4Z8pvCPp9eszsF64nHR8ohr0JGFuyrqmQXo9du5P8ebLVaQv2bYvxcyExQvjHqW6pCkFnqbQXdB5bV7ykYMFWq+Rbde/KU7PhhgIUG/w+wgJ+XElNEhDQkLWhGLZeKqKq6gojksh6RHDx0aQUCSFdatNtKUZm4oUYFq4wISp8dq4REqJJ+E74/D09j4eODtNxHEZHujkAW/1qapc95GAJwMjsVxfXQ/0WuTrJh84PobqS57f0osQa2tWZNs+CBH84zKjdA0YquATAwrfHpNEVPjYhrXvdNmE44uBWHd9yY+mrzZIl5qSfzwbdGZ/pEewp+29ETy+lJRMSBrBZ9F4fYHq96eD9Kmf2XAx1epWMRseP3o6uCHSdIWDH8y+I4XyQ+4c1WcuXLUudY3tQkLuEnqAC8PDwx7A8PCwNzQ0NL2y/kqD9DNDQ0MfBmaB/2V4ePiFa+1waGjoc8DnADo6Ot6xgYeEvJ+xLZ+XvlfGanqcB3o3xi6aowDFBQdNE0wuBCactHzOnDWp70iSvE5t2e0FcdNIuaOX/dWfWZbYnsS4iYn05ktVzh6vszRr4y+5HOyUTCYN5MgyVE0yisvDb55mYmOBWCLH/laV1m71ov/nXyZA9+9NkDVgetwkIqMY50s0zjd5vZRjpiuBwOW7qsoDXbC3WseddGkogtaeJKdKGjv7dSarOp6ED/XdeNylp+dozgVX6NTxKqmHL12to4NZpJSrzObsT/Wj6y79w2dpbmrh358ymZdRPtoj2CgdZiYtjieytAxP0qU5JB7YCufn0GwfNaXjVR0AxrwIpXmb8ZEm0bE5tk1cQKuXkJqG2NsHpg2uB9cwPydHmxx7pQoEafYPPJ5D1QRzFywmzjVJJFU270ri2D6qJtANhYUZC7PhrcRB+MxdsFYZpJYp0XSFdeujaLqCZUpiN4gwfrsIIXhit07rfNAcTADp3NWa1vEk35+UVCw42ClWdZhvaTd49CdbVsbt8/2vL9FUDYzuGEu2gt8aJZNUSOgC/QYxBeoay5TdTVypO0PNGRLeKYaEhKwJtdmkULNQBJgSdv9iB9VUhHLNoyOvcV/f6tPMWFeeb2/ro325iq8ozO7s5FEFmiseZ0SUtff0AAAgAElEQVSFSjLGv9y38eJrThRXh2fu2xbhK883MWsu0ZTGvm1XdE64Bh85Pka8ZgLw4aOjxPV9azpuXRcE7QDubAjpUIdgqOPOCY+YFtS5eqtYe/oawv+p85KpavD810aCiITEHUjFvx1cX/KF45KJiiShC57sd/C/Pg6+xKvYlJ+aoOXJzbe1z4UZ++INkev4zEyYpLP3Tr3UH0eqjzx81bpQrIa8D/hz4H8bHh52hoaGHge+OjQ0tHV4eHjpyg2Hh4c/D3we4Mknn7xzNVxCQu4hahV3lSFar3rEEirNerCu0GEQjanENVhsBvXvc8kgZX0tdCYvlUdqiYmbmqMAxUWHhZkgQlLEVKbaUxTXpeicK+Io0HfhAoecOpG+VlrMOPlPHOavjguariQfE2y+lO2Nogo270qyYUucF780TePoDGLBJ1VyiQkDa1sB04FXJ3z6p22WfI1RS2PsRZe9W1VURdCfgYQuGMhef+y+5dE8vsyM0HlNiZMebvLEA4EZPHfBuhiluGN/iq7eID1bqILkyClQmzz/3SqxlMX4/kN8YdznU3YNYXl0FRv4CLy+DPN/8ToyUiaSU2k5vBGzqxc1a6DNKTgTJiw2iZ6ZwfJjzKQFT23bjb1ngI8eWMegce2oytlJi0bdWzFuXaplF91QeP1HFaQvWQQmR018T6Kogj33p6mWXc6fDrrZt7Trq8xRgJlJk/KSgxERKIpkftom23LrUZ23Qyavc+ADWeanLTJ5na6+6FXbfGtMMjwbXDrOluD374O4fvV36bky+I4iLi/7Ov05jfUbVLbkBY/0CJRrRFK/H7lSd4aaMyQ0SENCQm4L35fMTllIHzp7IpRjEVqKVSxVQfEkzUSUf/OAznRNY0v+6i6eZkTh6R39aJ5ECsilFf6fzYJ/GQtqcH5ig+B3v7P6PYW9ul7OhKnScyhDs+kTiylMmCrdNxm3cVnNHcP38TwfTXv7Kde+D1IN0vYBfPXOFmO/U8R1wcEOyd+cCoq1H1539TbOZR+vLyXenQiHvU3OlWCiEnyWdUfy6rTPXv+SNyDd2/cJEunV38nNCvKHhISE3CaTQPfQ0JC6Ej2qAl0r6y8yPDw8e9nyd4aGhiaBHcCz7+poQ0LuEZJpjUhUwTKD2fe2LoPOnigzkyaxhEp7dwRfSvSeGI0zFk46wse3R25aM/RKrKaPpouLEXVPbBT8KA62F9QvvRW6+iIcG66wXPRYECrmhM1xRZLZtI4Hy6MUhM2WnTpd8RqUaxCR/O5ewbKpUNA8kAqXJusDdEPhvoNJvv53CmpUsq5eY3l6ifN7C9gCTlQEX1pKYnuSKKApKsvjkoe6BcsmHO658US5UCSLp+b4QrwbNBctH2Hpi8s8tjvCyInGxearR1+p4URU/BeniZ+aJ3tyHqM3ienCwMgUyswb1IVBuSOGW3aITS4R03xmZnyyb9SZTClsLl0ge2SC5N//JnTm6a8sMHNkHuN8nfa5IpaZ5Gt772Mum0Y5uJO/y7fyb315ze9ycc7mwlgQLNHaGSGeVKlVXOSK3nQdycKkRWdPBN+TnDvRoNnwaF9n0Kj56IZCW9elSAPL9HnjpQrjI00iUYWuvgiZ/DurNVs7DVo7rxPmDCw1Ly1brqRqi6sajALEkyr9m+JwpkFvTLLzYJSBnrvzXiYk5N0kvFsMCQm5LY4NV7lwPhAXc1MR5jI6xVwOhCRqSjp0hT1t10/Tnm+ArarYK9fgqAfrs4Lf3nPZ9p5PTAYNkJqqQm9qdQp9TANNU0illIuPb8YLW3p48Mh5hIQXt/TgIdd0AtQ0BfAvptjfrTQcyYsz0LMyJfrMFPzcFYGYj/UJ/uYkmB48vA7SkXf/mBJXiLdE3iD1SBfVZ6dRYhrpD13D2b0J+VaD3YfSzM/YZPMa3f1Xz7S/Hc4sS8ZW6pDdKMIiJCTk/c3w8PD80NDQEeAXgL9e+f/1K+uPDg0NdQ8PD19YWd4D9AOn3+XhhoS872k4kooNrTHBocdyzExaJJIqHT1BplH/pktln5bqktlZlxZ8hGlxbi7Kh/oDcVqzJU+PSywPDvcIOhJXX+uPvlxl6nwTzVDY91CGfKtORBM8ehud7AG270sxPtLkpVfrSKEFKc51h7lskh99chedMxp7p04AcG7rJn7/q5LZGZMBq8HPnhjBjOgoPzPIzz+aouYEpZV6UhDflMFpS6A3KuiG4CG9yZ71MAO8MA0yprFU8mgKhYJU8Kvw0oxkc17wrTHYnJdkrqMHp75xliPxDDUiaCbEmw6Tr5Z504qyNOfQ2RtF1wXP1nW++/dFGsebHB6r8uCcRd702KwavFBM4qgelbYosWKTnK5ityZw5yv4000WIgkaikdGxKg3PQpffIX5oX2o3xljx2yJiekaitBpxBOUEhkW2zuIJFLoPngSrrT6fE8iJbR2GDiOJJ3TMCIK2bxOrqBTXHTQDEG+9ZIgNSIKnitJZTQSaYjF1VVlA17+funiPZH0JW3dEdq7b57V9k6yr10wXgkCHvozgtYbtAvYujfJwLY4qibCeqIhISuEBmlISMhtMX7O5MRoEEE6aEpKGQVbz+ApKgnb5PkLTX6d3HVfXwjqel9MSI9dY1azxXSIrnSxTwiBrqyWOdtbJF8+BSeWYFtL8PjK2fMrsQZy/Kd8BiS0pBT0NUZ8Os7N657eDTTdIH39Lar21dv0pgV/eCAQlNo7UFj+VliXEnxsg8KbC5K2ODzYLdB7u0k+3AmKuGbThFuhqy96zRSkt8u5kuQv3vCoWJCNCv7r+xR606GoDAm5h/kt4D8PDQ39EVAEPgswNDT0TeCPhoeHh4E/GRoa2gd4gA08eXlUacjNcTzJl05JzpdhQxY+vVncsIFMyL3HdC0o1WO6ku6U4Fe2Kwxsvb47ZC7ZKDUHH5C2j75gAkFk3j+NSEZWyjtNVOAP9rMq5bhadpk6H4TqubbPuRN18oezb3vs+z+QxXQk7jy4Puzr1zCTkIkIott2kM90gefz70/lKD9fJlN1mHc8nnLTPOaWaT5zgW+t38zr8+D5kra44Nd2Cjb87nbO/tVZVNslfribPZsM/nFEAhJbEaTSGqYJjgwSot6Si54fRB5mruP1HTvrUXQjJIWk6Qvckk1USLyaRmuHEew/quNkYzRGSlQakheSOdqXetkQERRllGzCZ6wjxws9nTRR2Fyt8jltmtFCF/O2SizTwKjUGa1X0PMKvNpE0Ws4yzqdiSgedcyuPKcSKYqdPby4YT3qnMKv9kkM9er6o4oqSKa1i3cLLe3GxfUHHslSLbtEYgqVksvIsQZGRGH7viT1qstTX17AbPps2ZvE9+XFJkzFJQcQ6AYIRdA3cHu18t8JdrQKOhJQtQW9aW6aKm9Ebt6o6nLmZyxq5aDJVZiZFfJ+JPxVh4SE3BbPnXMYruhIBDOjLkupJPVIDAlofhzXvLE4aItBXHp4rgQhKFwjhSfmeTiqigQMz6eQWG1mHpkXRFTJ3rZLj/d33njcg1koWwq+DDpBOp7kmUlJ2YIDnYL+63QkvR6GoaILD2dFTMZuT1+8a7TEBJvzgu9PSOI6/NK6ax+nEIL3uub6gU7Bgc7VgxDXELnvJa/PSV6bD24kFCF5c0GGBmlIyD3M8PDwKeDgNdZ/7LLlX35XB/U+5LX5YIIKYKQIr88LDtzkuh9yb/HKbGCOAlyoSs6XBZvy198+mVDZ3Sq4UJMYKhzquaQ3iiZ4ixZe2cHL6NhejOhld82aJhCKuJiafSvd0K/H2XmP59+0ieoqj25VmErFaCQMPtADXbpEOB4ynccBrNdsog33YgV8DwVp+viawsklybmiRBFQtSV//oagIxHjvv95F3FdsCEbmGW6KulJSb5w3GeqGuiZ5SbE1KAWK8CGrKDzOo2GpJQ0MlmEYZNomthE6NN84lISMW3UQoTuvgiRtMbrp0wqJqgIHOClQiv1pTpzTbBVnWc7O6jrOk1D40hUY9KoYekR5IxDLZekaRicbNtGMRdBz2b5jYyBH1EZiWeZ3J2guSmPY6uMZ1NsjyooqkvVuX76+dAHMoyebKAoMLDt0gEqqiCTDyI2ojGVts5LzvDYmSbZgo7vS2oll4UZ+2KUaKHdwDJ9ahWXllaDdRtWT8ifP91gYcYmV9DZuD1+1WS/70mOvlJlYdZmcHucvsEbhHtewcszkpGSpC8teLB79X4LcUHh1nd1y0yPm7zxYgWAcycVHvpwjlgiTMsPeX8RGqQhISG3xT97CRzVBwkXRISpfIaI5+ILgZASxNUXSs+XFE1IR8BuQtr3qQmBik/Bkbw1Y/8WtqJiaYFBKqS8SnheWY3yVqpTFi2w3WDbkgnfHodXryhinrzNxkT5iM9yM9hHa+zuNMlcX7LQkDiexFFgrg6D1w/wvWWajuRsCXIRVnXIfL+jiNUR0PfOkYeEhIS8d1x5rn2Pkh1C7mIyl0lJIQTpm2Q65wo6+w8k6R63SGVVNm+/ZJhtVW2mzgbdzlvrJrUljWj7pTeIJVR27k9x/nSDaFxl65631wDyfEny775rUatKVAzuc12+XddpuJIvHff5GVFjnWlRXHLp7I3wyY4ofxmBRs0j5dqsr5ZxB9L0PdHL00UYC7wrqgvwWG+gvRcagt+979IfzPaCYHtBsNCQvDgteWEGYgLa4tCegN/crdCeuH7koRCC1t44o8stDC/pCNenavj88vI0mdY0RyYsZiZMFFXQpiu4KZXFhsG5fJJjQuVF1+bxM2MYtkM9EaER0fGkwNRUTjw8wMBUibZKicWKZCaT4lhHD6W4QYvvUtdNLuzp5g01yktVFXSVg16DjPRxBLS26eRu8L3HEyo7hi614fE8idnwicUVlMsi0l1HYpk+sYRCuegwdqaJlEGq/eWp6Pc9mL7YSX7jttUG6PyMxakjNQCW5myicYWeDauDSIZ/UOal75eQUnL2aJ0nfqWdjp6bZz2dWZZ8c9S/uJw2FHa23pmT4gvTkrGyZGNWsP+KoIWlOefismv7lItuaJCGvO8IDdKQkJBVlC3JSBFa41wzMs4xfRyhgADV9rGiGqaxokYk7LiiXqjlSv7qmGS2LklHBB/Iubiuj6NquFKiO+5V77GYMIi5PoqULCSiGKze5942OFMUjJaCVLu3IklvRNEEa2U3VQcWG5c60DtekEqUvP6k81VIKSksLyFFIGQKpgncfeEsSw3Jt8cltZXU+m+e93lo3drEjOVK/uh5nxNLkrgu+IMhwaGuuyvS851ib5tgqF1QtgNzePd1au2GhISEhNw57muH0bIIUuwzsOcWrvsh9xYPrROYXjARvLvt2nVDr2T95jjrN18darde9zjQIXAlpA2oFl0K7atFYnd/dM21zUfLEmclFcmTgh/MC+biHq6u4JqSI5pKbMnBbHqcnVVpLjT5nfVLnHluhpTVpCvbZPbnPsGHdkb50Qs+eemxXPaQKBgiuM0vWdd+7/6MYKomSS+BqgQl9TsTCp3Jm39u29drfOWfLerRCGZcp+hLTugxKsdtJqIJ+nQPe8mmhoIaVTBqNvGoxI/olKTKtBGjsy3O+pzggi9wfIgq0BKDPXsTjNgu1oyFWATDdkj5PomCzq5dCUaWDI6PgSWg1oQT6f+fvTcPj+Qq7/0/p7beN+0aSSPNvi8etz1jG2zwgh0gbAHiJJAEshBCuFxInuyB3Nyb35P7S8J9bm5ICAlbQghcAkmIDQmYxeAF222PZ/ds0kgaabQvvS9Vde4fpzUjjUYazUia0czU5x+1qquqT1d3V731Pe/7fYPsibvsa9GoCWs80D7/+O2K5MShLOlxm5GhMromCMcM9r42juXTSI/bvPDkBOWSS22jRbnkEgxrqiGXIWYIgv7gTMG1mHc49nKWSlkSDM2Mi4t5l5NHshx+IYtpQX2znx99d5yRgTKWTyMU0Rk6V16QQDp+0Wc6XrzsJvMymJN0TkKuLHmqT30fj49JYj6NjTUXjmdto3neWsKwNGIJT0ryuPm4ob7VyWTSTKVSlcuv6eHhcTVky5K/PSjJliVCCN6xEbbWzQw0QqUKY0F18Y4VbCTifDqdkJLtTTrf6Xbpz6pZak3AQE6tkC5JHh80yPkErgNCaHRbs6d6fUKSs9TpSZOSLa0zT1WmLnjX1isTpibL5xvOkynDnc0avZkLJuYNV1iKIoB7+k5zMNwASG4vDLMSBVIX1aW+5KjSqeJsPfqKOToqeWHgQsf5r56EfasWv98bgTVxwa/s1uhOS9bGBK0RTyD18PDwWG4MTfDoZu986zE3hiZ4ZM3SfEcaWnxEXylgV1wMS6N+1RXMoF8BHTFBTZ3BQF+ZQtZmjVtif9aHLV2EqSFsB01TcetkQdBrGryYDhBorOP+XB++koM9kkHXgrRYLqcyZVo06MBmbEjSsMrinpZLv/bbNwpaIxo+XZIuSeJ+waNbFnb89h/Oc8bv50Q8gum4xCsVvh2vwyo55DDpEz7WZcbxOWXKRZssGpO6gVEq4UQDZMIBBgIhNvZPUlMfpCEmuLtDZ4/f5l+eLOCUJbHJPO0jBXotA0PXkIbkt49HASX6lh3lmTqUhxQajiX4L22C2stUdB3bn+XkkRzZtE16zKZ9g5/spM1Ab4nV6wN0nyxQrmZUjA6WCYZ1Vq1W9z26IRgdKrP/mQL5nEPb2gAdGwLnRdNDqQwj51RGwphQYmoh5+AL6BiW4MnHx0mPVygWXMTLOcpFSSHvUilLDEMsWHDfUgPP9AkmS5KwJdhRv6DNLslwXvJ3h1SlWXdaTQgkqsMYu0h4XdXux7DEeQ9SL3vU42ZkxQmkyWTyv6dSqT+4xHIL+GfgTdd+VB4etwZ9WRhJO6SHivgjJifGrVkCqZSSloksICiYGhgXLo4SQW8OfnhWiWenJ+SsmdyAAQV0pF4VTZ1LeJBiU5ACEAjp4NqLz06MmpCvKJE0bMHGhOTXbtPIlAWtEdCvsF5PaBpNMY263hNoUmJuWnniKECNH2I+GMiBqV26vH6iKPn6aZVlel+bKr2aj6Cpsg2cakZuaMVdSZaXtXHBWq97vYeHh4eHxw1LZTBP5vv9CEsj+mAreuSCCBqJGdzzcIL0mE2sxlg2IWhdXPCBuy1Oj+iMH04TKGr0nS1wFoPGZh8drsGWmgjlngoTRY3+UIhIXlKTy/ASDTT7XfbsSPD9HpfBHBhI9vgrbLAcgk2w/TY/dcFLxyumLnh1q+DuVZLBvBLGFmo19aweJmaNk7DL5DQdKV0OagHW5iYxi0XK9SEMKpxzBCXDQNgOuu3QkC5wNhHkVDxOvWMzXoS1z3Zzd/cRwobLD/bdTkUEIFehawJyPh/BYhnXb5GrSL7X7bK5ViPhg6Aumcw6IAwmiioZ499PS7bXMWcDt3LJ5cBzacaGy5RLEl0H25YYJlh+Dbsisfwzt926J0LfmSKlokvbWj+Hns8wOlxmZKBM7+ki53pKvPqRBKalskynkFJy52viVCouobDO2a4idrXBq3QlrlQepH6/wLA0Eg0mtQ0LE+KjPsH7dyv7hLoABMyrj0m706qSDqAhIBkvCxJAxBJsrZ29fkOzj4aVecvj4bEkrMTb2l9IJpPDqVTqL6YWJJNJE/ga0Hr9huXhceNTsiWPdUpGC5BsEuxpnHlBDUqHw98dopBz0HTBnnAcNkRmrJNtjzI+XEQDZI1/lgFo40U9muI+wUMdglfGJC1hyBQlugBbCgRwqWbyZSnOG4xJNPLZMtQv7nT1cAd844wSSPc1g6nr1ASg5iobTkopOfvaOzlyMoMUgr1brs5/arnJVgSNQRjOV4VNMdux9Rtdks5q84uvnVRNrILzBFsbE4JHNwme7JUk/IJf2umJhR4eHh4eHh43BlJKRv/hBG5WFSa66TK1P7d5xjrBkE7wGmTIdYRc6l4eZGAox2kRYIfPokMHqznEziaLxjpBtMWleE5ijMGELw7RAAm3RP/mbXSN6nSnJT5TIxg3yeQdgmG4Y3eQ0Bzi6HR0TbDqCkPYjtU+TozGaRh1OCZMXEc1aDrWWEtTpoCVdykVbIYDQQzHJWw71E3k2d01wKlVNfSHQ+TzeVrOjrOt6zjPN9WRCQQI9kxi1OiM5AXdsSjjfgO97NBdFyXnM5EOnJiAOwMldg6N0nJyiCPRGPu3tcGESzZiAOac4x4+V8Yf0BBCYFkQjho0rPJRU2/ReSzP/qcniSZMmlf7KeQcWjr81Ddb1Dcr4TKfc3BdSSGrhE7XkZQKDtm0Q6JOY82mIIdeyCBdSdu6AMGwDqjvUFObj6YWH10nCgTDBqGIzthwGTSI15jUN/lwHTnDC3U+/IagLXpln9ulmEoScVxJ0NJ41zaIWqqybjHCq4fHjcqSCqTJZNIAXgc8l0qlRq9yNz8GfC+ZTI6mUql/rGaO/gtKHL1/iYbq4XFL8tUTDh9PQbYC6+Lw2Ud0GqZ5NB05nCWfdSi7oLuSQ/szjO8L88KAJGgI9q2CP3nEx588Z2FL+K9JwceflxweU9trwC/uhqf6BAVbUh8UbEioi/hUh8Vn+1xCJhRspYGujswep6ULNKm0Vx2JP3LlpU0HhiRPdEsCBrxto+CtGzX68y62A+/YtDQX/JwtGPJHQKjHKxFDk5yaEEgkuQp0p2ePc3rZveNKKu7870UIwS/t0nl0i8Svzz1T7+Hh4XGlLFEs6eHh4TEL15EceSnL2EAJ45xLR1h5b9oXmzpeQya/2UP+pRFCQHtxArGnnXTYoL1d0FoDf3dQZRvGfPDW9fAfXWBoPvrKPr4zDLV+SVcadtUL0kGTWIPJ5jt0QgvwYL1a3rbDoCL92M/nGcw5+NIF0pZJsFwhni8SEIJhf5BwscJw2I+ja7QPTfDimkaMYoVVuXEqlklWM+hKRBjzBTBcyWRAI2RLHDQKpk7eMrH9PoTQMFyJLQQlG4bPFWnIFZGYbBmYZCQephz0ofUX+JEMcM/rEmiXqAwLhHRCEZ32DX7siuS2u2Os2RSk85U8k2NKLE+PV2hd62f3XbPVx2BIp31DgPHRCoW8oKbBxBfQCUeVCNrS4ae20cSuSMLRmTKLP6DzyDvrGRuqoJsQiZsceylLb2ceXddoXRNYsDi6lDSFBO/dDqcnVEWdVyHlcauzpAJpKpWyk8nk14DNwFUFtalU6kAymXwz8FgymSwC7wFagPu9QNnDY3F88RgcHnJxJJxLw4uDgh9be2F2fNIwqNiSgmFguC5jFcEffbfCM502liH4lbtNtjXpvHa1iwNsq9UQwlHZpIClQ9iED+4RTJQE9YHZ4tnuBp3XtTu8PAxBAz6we/Y4NzZb7D/nIoGakE7jAgzjp1OyJf92SuJKSaYMj3cqA/Od1dLxQyOCt2yQc3boXCijBSUQAgznZmdmrgQqjmBjwqU3Az5dzRRfzGvbBF86ro7bvlWCmG9hxyWywFIsDw8Pj4WyFLGkh4eHx6XoOV3gbGeBUsmlHAkTKaap80tC+xqv25gqA6rpjetKRrsKyA0VwlKg98BoIIorVbaioalmoskmFXP/oFdSdpVf5GhJcHxcxbytEfj7I5IP3758E9ghS/BTWzQGD9kUjowxUZSENQ2fK3FDPrKaoC8WJCNg3ViOxkKZlopDn6kTLNtYtsPpeJig49CTaEDIIrgupqFhRUwqBZ0Wu0JZGNSNpvGXHZ5e24xj6vhsG7cgeSFt0ai5bJjI8prvHqfs03DW1XC2Ds71BC/p51lTb9K+IUDqB5MYlkBUHbx0Y+Zx0uc5blv3RFi/LcTwQJlywaWpzYdpXbAC8wd0mKM6zTA1Glou9F7YtS9K+4YAritJ1M2d+brctEQELZe4P/DwuBVZjhL7A8B64MzV7iCVSv0wmUz+NCpz9AhKHB1bmuF5eNy6vNhdoeSqn326IjlwtjJDIK1Z5aerMY5WdJCaYPWaAN8/XCaQq5DVND71lMsbk4Hz3d6/dUYyXlTNfyTKLP30BOxqFATnuM4HTMFfPqjz0qBL3K9x5yV8bN68XlC0NYo23NMCunZlHqSunFn577jKW2mqk3vEYtHiqBCCgRycSVf3ObvX1Iog4YdtdRoJvzoiexpnH8s1ccFvJKHiinlL6z08PDyuEYuOJT08PDwuxq5IhvrLTI5XAIvVd7fRcHcUo+4q/ZaWgMDOGir9OVwHKokAshpA57MOm2LKbzJdkhia4L5WMDQ4NS5ZGxfELNVU9f27NPYPuYzk1f/5iqToCMzldAjomuDu8REOZ13SPj/nEmHCxTKZeBifKej2+zArLkEEaytlCJk0ZIqkLYO8zyRQsQmUyuR8CYYiOrYp2OuUuTdhk6rz0592edXkGPf2DzA04SA1ONNSg2uDdNVnGRjIUnCgJlekuT/HWSnxb5m/6+r4iH2+ZP7Y/hytHX4aWywGek1yGYf6Zh/RhM7IQJlEnTlLPAWwfBot7bMFWLsiyWVsgmF9hmg6H/Ha6yeMenh4zGY5BNI/BP48mUx+DHgRyE1/8lJCZzKZ/Poc+xqpbv+5ZDI5tb3XpMnD4yoZLjHtVy843lPmh2d9nJmUrIsLRgsQsB3MUhmpa+TzflYPZdGrpuKGsPAbAXKqCgWfDptrYbykBMmgATsX0EmxISR4ZO3cUVvRVvtEwkDVO/NKCJiC13XAE91qjA93CKI++NYZsF1mNY66GlzX5WwWyq7qaN81uehdLgu6Jvj57XBiTBCxoD02t1n/sgbSHh4eHgvnD7nCWNLDw8PjcjS1+SjkHQD8AY2iMM6Lo5NjFSZGbWobTMKxuW+R0yXVDChXUY0tN9UsLqYM72vCWhXCydmMjgoG+sogBB0bA4Qtwft2Qk9GeULWBgQJv8tnDkF7DLbVaNzXJqgLCny6xje6JFJKNtcKwsuou+UPjjLxtU7W2y6PvjLM329aRzmtMZEIUqgLgJBEXbBsFwvJZKVfecQAACAASURBVMBibE0DP4rGqOgaOwdGaXXKpNvinAkEKEpwNY0XZJkOq8g9q3U21mjw2AgnMiVabJe3nB3ggCXYbwUZs0zG/RZIiV6oUFvIIzXwWYLWJpPm1XNnLUzPj9AETIzZ7H86jV1xqWmwqGsweebbE0hXEqsx2Xt/fN6M0ilKRZdnvzNOIevg82vseyBR9SC9sXGLNvnUMOiC0B0NCGPxjXM9PFYyyyGQPl79+zVmJnEJpiwFZzNXCdV/LuG4PDxueZyLOiJ9ZUCnvVuJn6cnJC1jaTafGaYkBLqUbCXNuBlguCLRgF0Jl5/YKHi8U2VpPtIheNtGwaP/LpkowXu3w/qaxZ9Wjo5KoibYEiaKMJiTNF1hmf1dqwR7m2dmiv7k5vn3MdRfYnJMzSxfbkZXSontwtTEcsW5ouFdUyxdsH0e4TpblnyjU5KtwKtbBRsSXhaph4fH0iKECAD3ACellN2XWf1qYkkPDw+PeQmGdNZtCVIsuOg6BCPqVDI2XOb5708iXYluCO5+MDGnSPpYp+TkuDotfeU4/Hpy8c1srKoh/24pmRyzMS1BKKJeP2QJtkzrJv69XkhXK6IOjUjurnr839EsaI9CwVbNe8QiK6Xmo9KXRbqSzrMV0iE/ceFytDaM0xxix9lxGiYLZKTgcG2UslS+oc83xgmUKmwdyqFJKPsNNm4Lwbjk8LhGSWgcs8J8PhHlJxKS4tAE8RGJ5dOwAjrDq2O0mi5aNku31HEQSsTMZHEsHbs1zKserGXPO1sQl/AfnWLb7WEO/ChDpeKyeVeYvmnd5ceGyhTyDtJVn+9Qf4kjL2RoXRegpn7++4LOV/IcP5DFsSU1DRZ9Z4ps2B5auoN+nRj7wgnKZ9UcZbk3S80711/nEXl4XJ4rjDlnsBwC6WuvdINUKvWeZRiHh8eKojctebpfEjLhwdXi+nQGdB3QL/zsi7Y74+nwaJ4tg6OcjYaJlspsC0pCD8R58pUKAUvwpn0BVoVndi3/t1OCV7VAyVHNfXJlSWiR3pQF+0LwJwREravz95wujvamJV846lJx4dHNGhsvmvEf7Cvx0lMqDbTzWJ67X5cgMk8Gga7rdMRsToypO/ZNNVc1xBXB452SY6PqGP/f4/CR2y8f7HdOSF4clNT4VQaFMU8werPx0qA8n3W9q2FlvG/XlZztLFIpu7SuDeDzezP8twJCCB34AtAppfy96z2e6QghPgc8L6X8KyGEBTwPbAPKQoi3Sim/Oc/mVxxLenh4eFwOTRfccV+ck0dymKbG5l1KwBoZqJwXxRxbMjpcmVMgzVdA2C6JI4PoJYfC2kYCjbPLra8GIcQCJugv+n/a44ZlbMw0Hf+mBH3f7Kdv1OFIXQ0jqxJUGkMYmQotw1lcVxJxYe1YlnzQRDMFBUPjtr4s4VIFAfiKLsM9RUIFF8cfoajr2ELQNQ4/zJexIia3P7yW0ZfHibaHEBlBS1AjlnMQR3LomSLSb/KDR3ax7+Fa3rNREmkLziuOAsRqTO59/YWgPTM5rVOpEMQSBoWsQ6nk0nemiK5DX0+J218dpVxUR3vVat+Mhkr9PUUOPJcmn3XQDcHwuTL6TWBZJR33vDgKUO7OXMfReFxvbuKYcwZLLpCmUqknl2I/SVVTvw54LJVK5ZLJZAgopVIp+zKbenisOEq25AvHJCVbVv+Hty9RJ/UrYyr5RoCUmPkSEStEpiyJ+wUbX1XLxNfOsPrcELoliL56C0+6ATIWZAzBj0oWey7a4/5Byf4h9b56MpL0Egik97ZCrgIlB3bVg8vixZ6Pp9zz4+yadPnMI9qM2fXx4cr5x64rmRytzCuQAtT5oddQR/U62lctmkz5wuOKIyk5gsA88flkSfLFYxK7ejMhgQeXwLbgRuCVUcnXT6mJhYPDkrClsW4FdPx8+pkM//pCkaIruKu1yE+/o+aSHVw9bg6EEFO98VxAA/Zd3xFdkoeBv6g+fhMQAZqA96JK6OcMVpcqlvS49ehNS4oOrI0pixmA42OSZ/okEQtev9bz2r7Videa3HFvfOayugvxntAE8dq547/72gTf/2YPoeMjtIQFlX+aQH5wB0K/NhOTr10tOJeDsSLc2QSrrrDCainwrY2S2dHIYxJOBkMUXI2O4yOEyjZWySan62hA3HXI6z4IGpimRsB1EIDuSjQp6R91aCuVCDcEyOkaQgp0IekrCDKGRKvRadhbR25DjFMjgo4gvGubxp99dpSjgw4OAq3Gx6t2+Ik2Xd3x37AthF2RZNMObWv9NLX5CEfzdJ8s0NTmwzA1kJIXvj9xPq4a6vex554YAN0nCxx9KUNm3MZxIBjRCEcMmtusJTra1w+ha/jWRSmdVg0X/Bvil9nC42bkZo85L2Y5MkhJJpONwAeAraiDeQT461QqNbjAbf8NuLO67QagE/g4UAQ+tBxj9vBYTvI258VRqPprXgeElEiqgZQQCEfwa7cJRouCWj9o+HjmjRso7B/GrQ9yx75aRr5ThoTy8jlztoIs6+S/ehhsl8Dbd2DpGkLprSzQj/yyJJs0chV1vFZHBaEl8FE6PXHhcXcaHCkxpgmkdc0WXScKICWGqZGonz+wcV33/D4lcHx88WNcLiaKSsSOWII9jbMbVL26VfCV42C7ktsaBXH//MH2ZInz4ijASGFZhr0iGcrP/H84D+tWQLz4pSMuLzsqg6W31+HH8y6xm8D76mZFVGdnpLw4F2hhSCnPp/8LIZ5jZcZGCWCo+vgR4KtSyiEhxJeAy2YeLCaW9Lg1ebpP8u0z6qexISH46S0q2+8rxy9M6AkBP7HRE0g9ZtLQ7CN5b5zxkQp1TRaxxNyB54aEIBTMU1ol8BkCZ6KMm7fRI9dGEEv4BR+47fp/h+10CTsWxRU6tWMlHCHIaBo+oSE1QVkXdLfH2BFx6WgPsj5okncDuIcnkRWXtGXQki4QjBvsy2VIiQgZU8cxTOJxgzZfiVDUoLwqyF8f17FdSI3C4RycCYSZ9JewfTr7tvu5/SrFUVAd7LcnZ7Zw37A9RGOrj2efGMd1JAhBueTiD6jjPnzuQmbBWDXBoq7Jolx2qW/ysXNvhEDQwHEk53pKaDo0t/mW1fZguaj5qQ0UDo6CLgjsqL38Bh4rDi/mvHzMOZ0lF0iTyeQ9wH8Ag8Cz1cXvAj6STCYfTqVSz865seJ/VbetBXqmLf8K8H+WeLgeHteEuA821wpeGZVoQrC3+fpcIKWmIVwXXbrYQsfRJWIgR7Qni+iIMKmZjFsWJ3avpsZwqQwU2eiXnCjqaEiSCUn2t/4d7ckDICH71An2/PY7SZckZUeV9iQuI64thNeu1miNSAo2bKlZGh+lO5vhez1q6mtPIxjazGCqrtHirgfiTI7Z1DaahCKXF5emNzUyV2hFc8WRfPawZLIkAclYUfC6jpnHc1ON4CNJKNqCmsDlj/WqsMpY6M9KdE2wZ4WUmV8LttbCM/2Coi0JmmLFWCsMWBagCizGTJPyrfOR3JBMBalCCHFxwFqdqZ8RkF6MEGIrcB9qJv8E0CyEqJdSDi/fqK+YAWC7EOIcamb/l6vLw0Blzq1YkljS4xZivCh5/pzkP7okEUvFWSfHJbmKoGgzo9phJK+a2FxpXCGl5Ds9ks4JWBuHB1aLG1Ls8Jib+mbrfHfzyxHZUQOj/QBYq8Noy9kRaYXSWqOxtmhzSAsRnCwjXPXbGwr7GK8LEKi1cKNB4qshI+DEhKAjYhKI6jh5CFccMsIgJzV2dpi0hyRf75eYhSL1NRYPPhzDHCzyvVcKGGMmdtxP0YbUAFT8PnprfSrlYxQO9VSI2zaJOpNwdGnkjWjcYOfeCGdOFGhp9zHYV2ZkQAmjiboLn3d9k8lAbxF/QGPLrjB3vy6BP6BuEF56avL8NmNDgRlC7OhgmXO9JWIJg7Z1K7cMTRgawT0L6MDrsWLxYs75Y86LWY4M0j8D/gn4lVQq5QIkk0kN+CTw58Ddl9n+AeCBVCo1PtW5vsppYPXSD9fDY/kRQvCODS77ZZlERGfdZbITr5auCcm3uyWGBm9cK2Z5EQUqZczq+c8VgqxmMvKZV8CVCF0w+NB6jpdVzfioo3OgoPOrDxm8eCBH0K9x551h5G++iDapPGi0J/fz4CfeweFhJZa9aR34jaW5YVjqRkEf3KOxuUZScSUPtl9azYzXmpf1fppC0zTuXiV5ZUzl5N7WuDTjPZuRPNkr8enw8BpBZJF2BZMlquKooncO+6CgKQguML43NMF7tsPZjCDmY0Gi6s1CXVDwq7thICdYFYbwIj+fpeKBXT6+dlDHdiQ72gwSgRWq2HtMzeTvA94gpfx9IYQhpTxvH3TRTH0zMCKlrExb9jDwKWAEOAg8iorn2oGVFKx+Bvgy0A84wHeqy/cCr1xm28XGkh43AY4reaoPRguSPY2Cjtjs863jSj5XnQQ8m5XoAmoDyqbnwJDkrlWwvU7w8pDk0AiMRSSfPCD5uW3MWWpftCVffkXSn1OTYm9aJzg0Ak+dVdfS/izUBwS7Gpb17XusYCKvacFsDSMLNv7N8WUTy3NlyTe6JJky3NMi2FSzMmIOgNa3dfDOx/o53m8zXueD4SKGLlhjVjgWMcjFgzQG4dgY+HXYUSvJdzns3BKgp6vIuUmJiJpEdYkTtSgXJbsTkqItiGsupXMluo/lKI1LQr0lxqSOGTXZUAtP90HZUZMegxMuf/HPGd7U7OCzBHc9OH8PgYWSy9gcfiGLXXFJj9nseXWM2kZ1/7Z6mqDZujaAL6CTy9g0tvjOi6OOI8+LowBD/RceZyZtUj+YxHUlvdVlK1kk9bhx8WLOBcWcM1gOgXQ38PNTAS1AKpVyk8nkx4H9C9g+AJQvsbweVWLv4XHDIaXk6/8wTG9nAU0T3PtjNezcG13y1/jycUmxWsr/L6fgfbtmBlK1+QzpQARNAkhas2kIVzMrHEkwXaC2IUF6wsbyaTS3+ahv1nlk2ox6OWQgVS8jtKDB3x5SHT0BPvo0/P2PuQSXqtZ+CYn5xJL7vv7JvYJPH1Kd7H9p1+L37biSfzwqKVQ/w4IN7962uP3G/dAQFAzl1T43LpHwbOqCNSugtPx6EPUJor7rPYqZvHenxtqEScGGe1bdWk2zVjLVmXkBuFOz9lJKKYTYCPwu8PtSSlsIEZJS5qrbPAh8DLgNFeg9IYT4/6WUZ6q7/QTwXeCXUEnxv4Hq1LkLSF2zN3cZpJR/JIQ4gprc/oqUciq2s4H/eZnNFxtLetwEPHlW8oNede06Nga/dpu6lk8nV7kwCbgpobwZx4vQGpZ8u1t5mbdHQRMSKdW1azAnef4cRH2Sk2MuEyUoOhqro0oMfbZf0jWp9rl/UF03cxfln2SvKB/F41pzbFTyn2cklgZv3SBoXgafTv/62JLv82K+2SU5MnJBmP/w7Sza53+p0GM+orc3sHskSxrJmQYLQjq1QcmWej8nxysUshrDrs62OvAZgozQmBgqEw4IAoZBrNlCb/Jz+64AL72QZciBoAl1EQ2tXGGyBD0Z5fOv+Vx+8nZAaAzkXNJlJbyGijY+2yZvCwxNiZJLIZCODFbOd7d3XcnoQBl/UN3fTOnhZ07k6e8uEYkbbL0tjD4tSUTXBdGESXpcnSwS03xuM5M27jSrqvSE12LFY/F4MedVx5wzWA6BdBJYAxy/aPkaYGL26rP4AfDzqA8RQCaTSR34LS4owbc86ZLkuz0SR8Jr2gS1t1AG143I2FCF3k5l1Oi6kgPPZRYkkHZOSAZysCEB9cH5P2NXqhuBKQqXuNbWp4uUrBCO0AiVberGC8rCWAKaoH1rmIfGBadfyBKuDfLajtmzmeYH7sf58o/Alehv3sOL09zgRgqSzknB9kVWYlQcybP9ULAle5sv74l5vRjMq3G5wGBOUB9c3P7KDufFUVDZn4tlKtvz6KggYsHGFZR94LF0+A3BI2u8z/Z6Uu3uKafPxl+qZKnaXXMnYAshTqIaUn5OCPEh1GTwx4AXUMbyOvAPwO8JIX4L6Kgu+6epDAAhxKeAn0B5t3962d7gVSCl/Oolln1+AZsuNpb0uAkYnub5XHEkE0VVtTCdiKW8ynvSqnrmVS2C7rRqRjmQk/zeU8ofveioxooBUxI0VJbo93pVVYUr4a5VLrvqBc0hMaMrOCiP9Z118OKAYKQgqQ0IdnsVpysWx5V87aSk4qhP8uunZycM3ChMb6Jpu5KiIwhdv+HM4uhLORoLJUJobGoWrLsvjqZr/MvjE+g5jZzU6Kg32BD3MVyAaEKn0a+SLjrqLGrvqiNsKuurPbEQ/36wjGPqvOkOi6hr8PKxIv1Zl4yuM2GZ9GY1PpzUuGsV/OHTLi8Pw+a4oGYA/Lqk7ApiNUtjdxBLGAhNIF2J68L+Z9OUCg7xWpPhgTLrt4Y4tj8LwORYBX9Ao2NjgLHhCqGITjhqcMd9MXpOFdB1wer1F+6paustfAGdUsFB0wRNrSts1t1jxePFnLNZRMw5g+UQSL8EfDqZTP4m8Ex12T0o5fafFrD9bwJPJpPJOwAfqpRqGxCr7scD+OcTkp60uvCfzcCHbr8xL/y3CsGwhmFq52ciw9HL+1seG5V8+RW1/pNnBe/fxbxCoa4JXtvi8q2X1YX4gb2z1bpAWRIol5FCoEnJpqEMtR/dTLkng29NFD3hY8P7v82q45MYERPtr14Fe5tm7EM8eg/G1lZwXNi9hi374VT1djXmE3QswYT6Y52SA0MXskY+eNuFbrQrBSkl3+ySuFLiSviPM7C9fnFjDJiC25sELw4of7S7W5bmPav9LsmuPDw85kBK6Uz/XwjRgopbbgdywOellN0oI/nVqFn+bwF/CoxLKTNCiD8CeqSUH6nuwwC+AbwfeAzlyTmGmtqayhbIAgeAHcv9Hq8EIcTb5nteSvm1eZ5ebCzpcROwu0FwfAxcKWkKKVuTixFC8O6tcHxM2cQ0BuFTB1VWadckIFW373QZzuUgZMCWWjg8AsMZl0i6xKRpcXJcZ1e9mqjc1yzomoS+jCqx31yrmhu+fzdMlpRIu9JiEo8LuBLsaTJBxZl73ZXOq1oF/cfVBMHO+pWVEJMetxkZKFPMOxg4RF2de9ca9J6rcLKkk8s76NKlbrjMljstnh6AbxcD7MfkTWQJ9Bapi4yzfl8EIUxWtVi8r2W6BZnGulfFOTRhkzEMzJKg4rgUbUFPRvC+3TqmcMnafrQJwbcOV7DDBuUxnbfXXbnP8MXEa02S98YYOVdm6FyJruN5XFeSyzj4/Dqr183UovI5h2efmCCXsdE0wZ5Xxahvtli/bbak7Qto3PNQgrGRMuGosSQZrx63Fl7MOZNFxpwzWI5f42+iPoDPTNt/Bfhr4Lcvt3EqlTqaTCZ3AL8KlAA/qkHTJ1Kp1LllGO8Nyei0rtETJRU8XtyZ2uPacraryPhIhfomi6a2mTOBgZDBw2+v58WnJvEHNO7/8ct3ATw55nJ8TJKpQENAcjajE/fPv832p0/TciKNBtSYDfDwTNveNWMjjEXDVDSdaKlEx8gIvvbb8bUr0/CBfzzJ6EtjSAlkHbr+9BANn0zw7I+GCAZ17nt1Ez5Th9vWnt/nh293aQoJhvOSt24QhJegvL4/e+HxRFFSsAXha9McdMEIITA1Sa6sSm2sJWoY/uPrNPY2SSx9fkHcw8Nj+al6N2lMK1e6xDpB1CTuG4A3AR9FJZZ/HDU7fxxoAx4RQnxUSvmEEOKDQDOQkVKeEUJMpbxEgNcLIZ5CdW/XUb5PTwDdKKuhErAJzmcLuEKIMtAuhPBJKZcg93xJ+Oc5lk8dx/nOmouKJT1uDjbVKM/niZJgdVSVx18KU1eVKyVbcmoC3rgWEBoNQYevnFACKUDMUv6k6+LQNezw0LMniY3lyFsGh+7fiM8IkmxS3qS/sEPwg17JS/0Of/mlDBtMm5bVPnbcEfGaM61wTF3wyBr4zy7VTPPhG7i6YkNC8OHbF95E81pSLDjUNBgMDZeplCStpkDTwPXrGEjCpsq+Dgc1TpxzODlhEIwZZHPQNehy7xqLXNrmSCrL3Q8lZu2/70yR5w7ZhEMmQZ/AFBDzafzDUUlfRl1GdjcI3rJB8PkhgxELTDSOjEjuWiVojcza5RVT12hR12gxNlwhGNLJZmwcRxKrNahrskjUmYyPVPD5NaIJg/4z6mTjupL+nuK8Tb98AY3mtsvc2HncUngx56JYTMw5gyURSJPJ5L3AM6lUyk6lUmXgQ8lk8ndQKbwAp1OpVH7uPcwklUoNoD7sZSWZTG4EPg/UAqPAz6ZSqZPL/bpLwd5mwXd71OedbMQTR68zA2dLvPxcmpIDvZ0F9r42Tm3DzIviui1B1m1ZeA325GSZXd94mpqJCbrb2nD3JJnvty0rDoXjE+QKqklB8Mg4sYsE0r2vDFDUI2SCPjb1DdM8UZjxfN4R2FLguiCQpB3BU3/yMqEjo4ybOt/snOQtv7hlxjaapvEzWxf8thbEtlr4fvWM0R4VhFZoc9A1Mfhsr7qSvX/30u334uZaHh4ey8e0gHTWjHw1QHWq6/mA6PSunUKI9wG/ABxDVb2cAMaBGuDvpJRfqq5nocS+D6MCzwoq+Lyj+jqVatA7ijrR/wVwFjiJMsyf6kAaAo4A7xZC/I2UclQIsQMVJDegAuAzS3h4rhop5YzZsmpmwm2o7IXfu3j9pY4lPW4OorrElA66nP+WpeJIPnNYMphTsfHeZhgpCKSU1AagIajukhwXAga8P5YlF8xxpgK6ZrMmM0zabuf7vZLXrxWczUi+2+NSOVvA7isjIoCjBA9P1Fj57G0W1fsjbnhBe64mms/0SY6PSVoj8EC7uOb3grUNFsfHBScdC2lBoKIxPFCmvdHitnUWB7srVCQ40uHgpEa2AkEDGmo1QnkDo/qTnu7FOcX4SIWDz2fQixqrKhInaFGXMHh4jeRzhy+s1zUJ5ZJLz9EsYzl1j+Q6JgFjYSXrxYLDyUN5HFeyfmuQcPTS55mWNX7GR8oEJ3ViNQZ3viaGrgt23x3l4HMZ7LKLdCWaJs6/n7myQl1HUi65+ALaDf/d9LhyvJhzebjSmHM+liqD9HuoAzSUTCY7gTtSqdQocOhqdlbNIH0fsBb4hVQqdS6ZTL4F6E6lUktpzv9JVGbqF5LJ5LuAvwHuX8L9Lxv3tgk21mg4LrREvJPr9WZwuMILA5KiDSET1o/aswTSoi05OqqeX0gXytVHT/Cc62N/fDXrJ7MEugagsWXO9YWpc6zPxn5hAHTB6BvX0HjROtIIkOwbxdEEvoqDrc+MuCL3raJnQzPR3nGKIR+J17QR/sorULAxhKD8nz1wkUC6HLxmtUZbVFKoqNK2lRhASCl5ZQz2NKhA6Oio4A3rLrORh4fHklINNKdOEHKuGff5mB6QXmL/24CPAK8DosBLQogvA39T3e44yqPJB7xdSjk1ydoN7BdC3Ak8gApK7wdGqr5RGVRXzXunjSMvhHgRCANPSCnHpo1jAxCTUqaEEL+PMsb/dyHEKWAV8H+A/4HqKnrmSo/BtaDqX/WCEOJ3UZmguy5aZUljSYBkMtmKOsYNVG9IpkilUh+/2v16XBuyaZvnvjtBueQSihrsuz9OX0HweKdL2BK8c5M434l+KM95cRTgM4ddRgvgSNAFPLpFsD4u8OkQ9wkaJ32MNGkkm5TQ1B+zkBK+faTC5opDOeGjfCJDuTODLDhUAn5OjkPqgKR1wuUnN194bY+VyY1ug5ArS8ZLyjbi4uzpU+OSb51RJd7dafWdvqP52o5PatDTHKFAGWkIXtEAqY77774xwLMH4PFjNv6GMHUBDXMcav2CtXGdezvCZE7nMEzBltvCOK6yq5p6n/msA1Ky0XJwqWDU6DyctOiICVojLmerGaRr4+o8sV0rkzMtsq7gDl+F2sDCJjFefjbD+LAyep0YqfCaN166wq99fYB4rUG5KKltMNGq4zx5OM/ooNo+M+mw7Y4w40M24ahOx6aZfRyk7TJ5dIIDBwvkTZN4ncmd98VnNHbyWNl4MedNFXPOyVIJpOMo4/wh1Id21TW2yWTydcDXgW+iPuCps8s6VPOmtyxinNNfpwHYAzxUXfRPwF8mk8n6VCo1PPeWK4cmL8tsxTBg+SjKPCDJS8GQZbFx2vOOK/ncYclANXh/7WqN+9rm//xO95fJ5myCbpl+Q2d8qkZsDsplh6EzOcyMA0IwdiI7a52BhjjhQgXdleT9FtngzJIWO++gba5hsC2OaQraDY1irsikZoCUNGeuXfLOuvjK/n4LIRjOuxwcVlfKfauu+BrpcQ04l5WcnoDWCHTEVvZ3yuPKqQaMc5UhTQWycwax1dnxAMpvaRfwNPBpKWW6ukorYAK/CJwC3oYq8R5BlfN0ovyZDkgpTwohtCmTfCHEu4FfB/qAwyjPpo8CLVLKnmqgmbioROlrqC7t3xVC/DEqoN0NvBHVWfSwlHJQCHEf8MsoQfHvga8C/2vKRH+FM8GFrNDpLFksCZBMJn8GlUFhA8PM/J5IVDmaxwrmbFeRckmJQLm0TU9vmT8+aVY7zEt60xp/cLc6ryf8UB4v0zvh4kQtsmVBvgynJ5VI+plDks88LBgpCtJlaG0NEXtDO4VDo7gNAca2NzJ4tgTZCsfzJdCyhIcrjKQriLxDsVeSXhvF9Gl0pyU/PHtjl257rGwGcuq+oWhL6oOCX9ihmjFOkS7PXD9dVo3JriW6gOZmH/mCpFR0aa7TzpeUW6bGfckQz7ku+YrEAl7dKvivt0+d1oPI2wIIITgxJvnk8xLbhQfb4Z4WQX2zRTCsk8867Ai73HmHSSyh3t+7twpeHhZYGuxqAKdiEA/r3Kepg7Jt3cJr6/OZC5fMQt7FdSSaLpgYraBpgmjiglQSS8xO4y0VLviQuq4kEjNovUSDW+m4jH7+OP0vjMFoBf2OFiaIZFzgbAAAIABJREFUMdhXYlW7l5F+o+DFnDdVzDknSyWQfhXVWOkc6kuTSiaTl1TGU6nU2kstn8Z/Bz6SSqX+KplMZqYt/z7qQ18q2oC+VCrlVMflJJPJ/ury8wJpMpn8ZdQXgqYmr8vJrUSp6KJpYC7AU7Ou1sDaGcfJVDBjFrXxmaXwkyXOi6MAJ8blZQXS8YkS4Xwew7Ep+nwMT1bmXV+TEudsFqdaHl/pTM9ap+CzyAcDGK5LRdcQhZmia8StELMkpqahCWgIQOPOGkaOjmKaGk0PXe7ne+sgpSRbkQzllQdppuwJpItBSsljnZKDwypb4tHNgrC1uGB/KCf59CGJ7Sqz/ndtXfnCu8fCqc6K3wn8ePVvCPgh8NdSyq7pgawQwg/USin7qv8LVND566jGP/tQZvTvA94MvKb6MvuB56SUE9XtHkMFju9ABatngXNAXgihT5VLCSFiqCqVPwI+IaXMCiFejeoWuhHoQZUtmcBPCyH+BWiWUh4TQrwT5cP+e8D66ms8BvxASlkEkFJ2cgkvTiGEuJqMhuVACLHn4kWo4Pq3UMf1YpYylgR17P8c+IOpWM/jxiIQnBlL2YUKJ46VcG2JHrE4ELhQRnvmWI7KsRxaXjJiWNgbInSlBWVXlfVOFOH/e86lISjoTkPQhNd11PGGn6/nkQqYPRKnO8+2oBJZ0mM2da5NMCrQYga2KRgeKeNOjmPU+3Gbo9f0WHjcWuwfVOIowHBecmpc+exOsaUGng0q//+IJdjTeO1jGyEEP7tT4zuxAIYmef3a2SXjb98oeLxTnfzftF49N1WCrlUzfL/dLak4atkT3XBHE1g+jXtelyA9YRMKG/gCF+7FfIZg77RsWc0S3PVAnMG+MsGwPq/v58W0bwhw4lAOgLa1fjRdcPSlDN0nlQVZTaNFa4efplbfrEzPE2OSw4aPfLZEewiaW31E45eWVuzhIuXeLJouEBLM7gmc9hiWb/F9GzyuDV7MedPFnHOyVALpr6CyPjegZuQ/i0rlvRq2o7pnXcwYyl/hmpJKpT4FfArg3e9+94r4AngsPycP5zh1JIemCXbsjbBq9fyze9vjLk+OF+iehE04bIjOnL2M+iBmSEb7SwhTo7X58t44q7q6eSHQyrlInF0D3fhHJiidnqR4chKrLUxg28yfg60JxgIBKj4llmJewrBISqSAiq6DlIQCM28+Ah0R9qwWjIzbhAxJ8111yNsTBJ44ix42ib/NE0inEEIwkBM0BNVpoT/rCW+LoXMSXhxQx/JsBp7qg0cWmZ3TkwG7GohLqToaewLpTcUbUMHfGVR5dhoVsDYCXQBCiA8BH0IZ1x8SQnwL+G9SSimEOImKK34MeIuUMi2E2Ak8JYT4gJTyE1LKISHEnUKIj6ICYh0oA0EhRJ2UckQIMYjyMo+hYhWAFpRp/qFqoBoAPlB9bguqnOmgEOLPgT8APg38pxDiv1SzAn4H5T01MtebrwbcOipbwYHz2Q0rhRTqZuHiH92PgPdeYv2ljCVBfQ/+brnF0YX42SeTySmPr0dQx+RPUqnU3y3nuG4G2tb5KRVdJscqNLT4iOzvp6UUpluzGEk7WGGbfz0pePN6wZmuEpoAQ0C0WKZQdthZa9Cfh4ilGilmymBq0DUp8RuwfxASPsG9bYK3bhB0DMNAr/oJdWwM0tddpK+ryIDP4sQkODoETNholtgTd1lkkrOHx5yoJp3quyiEmNWkNWAK3rcLxgrqOWuOBmbLTWtE8HPb537ttXHBB/dceL6/u8ihF9RpfccdEVa1+2c0OTU05RsLYJgaNfULEzv9QZ32DbMzNy/Huq0h6lf5cB1JvNZESknPaZU8MjJYpvOVPGODQU4dNdh3fwyfXw12vCj58nGJI03k+gQd9XDbFmNOSzA9aiJ8OvFaSaXsUmgK0LY9RF3TCutA6zEfXsx5c8Wcc7IkAmkqlZLA4wDJZHIX8OepVOpqg9ox1Id85qLle1CK9lLRC7Qkk0m9mj2qozwVepfwNTxuQOyK5NQRNZvoupKTh3KXFUjPdhbZ4pTYEgZKcK7HpG3dhQu1JmHr4CRHThfxmYL1a2Ooc+rcHA3XE5/IkShkKRoWZ8Ykqz97gvGCJGJBx89uILDlQom8W7A5UxPjTK3KatgwOjFrnwZQrlYAaFISvijw0IIGP3xoKwdPl6iJG7x3jZ+EX+B2xDEsgXmRoHqrs7kGnulTGaRbLm1b5HEdWR0BQxPnM0jXeCX2Nw1CiFXAX6KyDn9HSlmcXmpUXecnUGVKH0NVoWwEvgAMCiE+iSpVcoC/qgaqohpAPgHcI4T4Iuq0+b9RhvhvllI+K4T4DVRlyRpU2VMXsBc1Uz0VrJ5EBdAfr46jCTgIPIWKZ6b4Y+BvgT4pZbk6bq36eGTqf1TAN6OrafXxSi5vWnPR/y4wPJWRcDFLHEuCmmzfi/qcl5OF+Nn/DCozYwPqxmZ/Mpl8IpVKnVnmsd3QaJpg444LsdL4Afj9Uj+fsBroFhY76ixeHpJsTAia6g2i3TbjBSgXHSI940QNDbc5TkHovGEtRH0ah4bVTyhqKU/A/s4ifY7OqnYfu/ZGyfoLPNsvCUf9/PibI6w5meMPD+hISgQKZXQBG+s0EiFPHPVYPvY2Q8EWnMvCtjpB6yX6TRiaoGH+W4nrRsWRs3xTAY6+lMWtZoseeSnLqnY/b1on+LdTUHbgoQ6BMYd37OR4hQPPZqiUXTbuDNG29soF0UsxPetTCEEwpJPL2OTSDqalMdhXIj2RI5e2Sd4Xo6HZx2RJ2acBCEMjq4l5+yVoQZPad28k98IQ0fssIvetQhjeOeRGwYs5b76Ycz6WKoP0PKlU6j2L3MUXgT9NJpPvRKnARjKZvA/4M1Q2wZKQSqWGksnky8BPob68PwXsv1H8Rz2WD01Ts5Z2RZ3zFlL+cHHZxcX/57IOJ15Mkx+zKeqCg88KNmydP6oZbGxitJLGdGzylp+0GeKl7jzkK0ifgXYiy6ZpAqmjCzob4miuGndnQ2LWPu2gDyouAomrCdbeNjPTtXMSjuV0zKYgGeCpPkn7aIa+riJCE+xcQDbtrUTUgrClriLxhTXM9JiDtTG4vUmcL7F/VcvixcyGkOAXd8LpCUFrGNo9gfRmooTyavq3aSVA7kXrfAz4H1LKL1cDviKqzObDKCHuDCpDceouy0B1+jyOmrm3gHtQRvlflFI+W12vHZUx0A68gOoi+gZUcHZECGFI1SH0V4H3oLpo/gAV33xsKiitjjnHhcwDIRUz3scl3teNQjvwzMUeVUJ1Fr1bSvmDuTZcglgS4NvA/0wmk9tQjZ5m+NSkUqmvLfYFrsDP/ieBv02lUi4wnEwm/xVVMvenix3DrUTk/laah07y0ESGF9ua8FU7TktgezJCIKxz8miO7mM5bFfnsB3gDjuH1hGlyZC8caPkbEZiamAJiXYijRV0ODgAE6MBtuwJ88Oyn3JCki/CY52qDrIhX6IvalC01OTyrrtCXmmsx7KiCcH9q2+8mMWVkv97XPLKqKQ2IPjZbYKY78L70KaJpnr1cWNI8Mu7Lv9ej76UJVf1DD3yYpamVt+CbNCulNtfHePUkRyViqRSdug7U1K/dwGdxwo0NPtojagmyd2nCsihIpF1BuU1kXnPC1ZrGKs1vOTj9bgmeDHnyueqY86LWXKBdAn4feBzqG5cAjha/ftFlOq9lPwK8PlkMvlRVHOAn13i/XvcgGi6YM89UY4fVN0Vt+65/MVs9boAk+M248MV6ldZNK+eqZaVSy7D/WVKRRcE9HVffjIjXBPmW7IRkBhS8vr6EuFv9GBkS7g+kz7/OjZNW99v6dgJH07WRgpBrHb2zzua0JkYveAu3dA6U6Q1tf/H3nuHW3KfdZ6fX8WTzw3n5tA5S92t1mnlaMu2ZNnYgIFlYAATbNjFmCEsO7vD7O7MwzLzDM/sLjDDsIwHDIPBGGOMkzC2JFuWrHAktdTqHG7O6eQ6FX/7R93uvp3Tva3b3fV5nvvcSr+qX51zquqt9/e+3zdMQZuxJEldIByfsYGwrzKQDBy2IgfpIlJKDs2HadsCODR3ZVkG7lQdZ6iC0ZdC71qlQ//vAkIIPrxB8OGrktG+PJ1JQWf0Md9ySCnnhBD7CEfLv004ql0EXiV0hjlAGvhtIcT/QZiZ4hAaqF8iNHYt4ARwP/AXnBkZbwBNhHrkdcKUovcLIV4GfpDQ+GwQVgP9W0JjdWxx/6cqZyKlHCXUVb/Sc1pNqUrLwemq9Ocszy6uW+mUhD9e/P+/XmCdXKbjX5GePdBPaNeeYnhxm/OItO8vjtZs0vZLd/C0KykfloxVoT0BXzsZ0PAEj/YlefIhmD08zLynMmoY1HzBwnGLhupjTapoTQnuz/nU95ewpy3+vjlJwoQHK1U270zgBWccNaWhOpNugz0JCVMu5Y1N/MRene0bIudoRMSFOLYAhxft4TlL8tIYPLX+zDW16740BwphEdkdd1+ds3Cp20bK8G8lSKZVdt2X4Y69aY4fqGFbJdJNGmJ8HvONETjoov3IA/xIf5Jn3qphZoB5n6P7Fe7IX3mBqIibh8jmvClYNptz1TlIC4WCC/zEotPyLkKBnzfP1XNapmMdJgxRjog4i9YOgwfed+W6MIoq6Oo3icXDCo7nplnIQKJoAk0PUzCUK7CtD88ZuKpB0m5QjieYGSzSH0i0ho9raHSVahyczfHKhKQpBk+uFXRlJNO1BgLB2tz5BQR6N8SolmvIAHRD0LP2bGdnxggLSk3VIKlLMnEFe0k0bSwRvRScQkrJ4XnwFw22A3OXb+NO1pn9r4eQXoBQBa0f33rZ0WTXl7w2CW4QCtcn9JsvoiAiYoX4YULB+wcJtR8ThAL1bwH/iTC9qBf4TUKDaUhKeTplWwihA/uAHxRCPCul/FshRC+hYP4xKWUghPg+YVrVbwKfIhy0/T3CUf8pACnli4SaUhdkUdgfzklXug04I6J3Nq1AbaUPXigUbsoHVqR9f3niuuBn7xTIQPLZAwF1F0Dy3LBkdz5J9kNrMN6eY3fC4HnHQLV9dhge4zMSLyFh3EJxfA4lEtRLPp4nOWDqDH9mhr3vb+O1SYGmwO4miZyBtRlBXxr2PCDpXIbshqul2JB8c1DiBvB4v6A7FdkBEasT/Zy7rnGOS6K13eCRD15bSZFtd6V486UyrhOwZefKRXFPjdlUSh4dPSZbdqZozumc2FciVhhke6oMswF84w348MNkl7wqus7NGngXcYVENufqZtlszlXnIM3n892FQmG8UCicIPSyR0SseqbHbT77/45QmvXIdRr8zK/10dR6pkhSplmnd12MuSkHRRVs3nn5kDavVOe+6jC2pmF4PtJXyXoe5ZhGIvCJT1X586OSQEqGyoAnWXPoEPfNTAAwYa8jjDY/w4NPtNCoBjQaAR3dBr3n6PdM1aE3zWmto3lX8PBDGU4crGOYCtt2R6khp1AUhc5EgKEslsm7gihFe6CM9EIDSvoS+2T5sg7Svz8uOTAb3u8PzQl+cXf0YhQRASClHAB+GUAI0Umo7XQHYeTgTwEvAv1SymdPtRFCGMCdQE1KeXgxIuDngB8RQvwYsJfQCP2txWOUgD9cFNqflVKe0ns6i0XxenGh1KRTYva3C0KIf1iclMB/F0LYS1arhN/RSze8YyvDlerZD3MmPQ7OjyiNuAocO2DmtVm+/HyZb2pZWtcm6OuLgS+pLLiUlDjJh9bwk5vi9L9QZvKlWbQFm1RbjIkdaaYVgRsI6jGNRt3BNjWUmGBu3mW+5PMvdioc2VelWvcpORLDEBBI3nypTFkodN2R5p7Nxg0bsPy7Y5Lh8qmCkPAbe7mk3mFExLvF+ibBQ72C/TPQmYQHl3FAoTmn854fWFnB/7HBBm+/UgZg4LDFgx9opr3bpD2ThtdKZ8JWA0m2RaOzz2RyxEY3FNZtSaxo3yLeXSKbc3WyEjbnqnOQAqP5fP44objt88DzhUJh/F3tUUTEZfiH/z7J6IlQ4qNSbvCtL83wsZ/vPr3eMBWe/JE2DrxeIZ5Uufuhpsvus29qivGmJEKCpanEpuaZakmhz1nMZ0wa7c04RYfiWB0zplDRTLaODeN44cN7y9j5715bd6UwTIVaxadnXYxE8uyh3d4UpAxB1QmL2mxuFrTmDFrboyqLF+JX8wp/tE+iCvgXd1/eCDT6UmF5zkCCWJy/DMPlM9OTNXlR4fuIiNsZKeUkgBDiBUIn1DZCffFPCiG+Rph2JIDHgZ3AHwGHCbWYfELtqA8BXwC+I6WcOmf/R09NizOeidPG6eIo/e00Un8pTsXTC0L5ImvJOoewaMCf3IiO5PP5pwlfPLYTfj8HgX9fKBS+vhz7vwo9+y8Av5DP5/+OMJrho8DDy9GHW5lASl4Yhem6ZFebYHOLYH7G5fUXSvz9q3W+l8qhA0eGfH6gVma9b/OVF2yczgRew6fnlQpZ16F1oghC0Dtf58Nmltfuy/JMzcOswsFMkgDYG9RQDBXNVDlUqDA3Fdp0ZkzhnseyvPJskWFX4YW6gfaay1sNnU/ugpi28s/j0pLXvZor8QKBHtXMjFilPLFG4Yk1l99uNTI/c0aq2nMDyvMeiaSK7ytUt2xCffMIibYEygd2I4Rg9/0Z7N0BuqGcV38i4tYlsjlXFctuc66IgzSfz98JfBLYAPxsoVCYyOfzHwWGCoXCm5dpvgl4bPHv3wG9SxymzxUKhb9aiT5HRFwPowNLNEUlnDhcP2+bpp4YKSNGUofEFQRiZisuKh6BUFACH0dVGOpoJUEZuyVBf3uKoVeKvCVjxGXAE/EFioqBlKEl7avna4UqqmDjjouHOiYNwS/shOMLgrYE9Geih/2leGqdwlPn1sy7BEZvitaf3oJzsoyxJo257nwZhHPZ0gKFyXB6fZOInKMREUtYTE8qLUlj+sDi32ellEeFEB8DfolQx7yVML3pC4SpUBBG8QVAQkr5e1dyzCUpS5FxegGklB8HEEIMAr+3WBTgqrlOW5J8Pv/zwH8G/hL47OLih4Ev5fP5XyoUCv/tWvp1AS6oZ5/P578O/OtCoVAg1Bu7l7DSLMC/KRQKA8t0/FuWF8fgueEwQObQHPyPu2HkmIXnBgwbMVwpMIKADeUaOc8j26ryWlWwMOTh1H3eqbm0uQ4dtsqH+lzSBgQ1F9mkoG7NUBwM6Ch6xB0XP2Ei1mdxpGBk3udULJg7Y1F/22P8pMPrxLBTEiMmWGhIxquC9Zcf7z6P745I9s9KOmOSzfU6VsWnd12M7jUX1nh/qEfw9YFQ2ue+7sgOiIhYKXKdOqMnQ/+KZihkWzSswwu8/gdHmZ6wSXdtJPXeDdzb2YxCGMkdS0SjFbcLkc25+lgum3Mpy+4gzefz7wf+AfgG8B7OVOraAPwM4aj5RVmSWv+Zxf1tBf7nxbY/R1ghNCJiVdHVb7Awc2bAom/D2RGXXiD503ckc1Z4b5upC9639tIGbkstQBEBhudSM3V63IBnW3M0Mq3oqiA7ZuEdK/Le0QXqMYNvk6PL6CftzSKFoBTruaZzyZqCu6O6ECuGuSaNuebKRdyfXi9YlxW4AdyRW8GORUTcZAgh0sDngUNCiFZgM5AE/pqw4CNSyhcWU5pYqgW1hBmgBDwAvLFY7dK/zXSbVgQp5f95rW2v15Zc5LeAXysUCn+4ZNln8vn868D/AiyLg/RievaFQuGDS6Z9wpemiKtgdtFm8hccnFmbg7pOLh7aTltbFQbKkpaqTU76KHWPOc/HQoNA4joBhi/x4xoVW+NELWB9Z4zM9lacBZiuSKaHGxCAagr0uEYypXGiKPHUOOpwhWCkwtZykf2HFfB0Yl0GDStg3VYDQxW0xi/V+wszUJQ8u+j0HT9cY7pisTYrmJt2SWU1Mk3nv5rt7RJsagYvEOQSkXM0ImKl6OqLoT+mUCl6tHcbxJMqz351imOTPq6rEBtp0HuySvnu9FlSahG3PpHNubq5HpvzXFYigvTfEhqk/zmfzy/9YTxPKGx7SfL5vALkCUOSH+OMEO5fLu4jImLVYRhnX0qmefZ82ea0cxRgoHT5fVoxExEouIrAdCRjqTgJ1yPhBKApnJxXWTc4g/AlCcfFHTYZ6euhezRGoChMdrdd07n4vqRS9IgnVMz4TVnj4pZCCMGOyDEaEXEeUsqKEOI/Emo4jRGO2L8spTyt/yiEEOeI5KtnmssAqBDqRpmLC09VFY1YBoQQHydMO+8Hzho5lFKuv0TT67IlF+kHnrnA8m8QFj2IWOXsbhPsH/awjpRJalA67tCzK0UipbK2UuXDTWAFAXd2asRcQbnosX5LnNFEjODtIrrrITWFuW05ntmQopiMceJrsFmzmakGqIFEA3xbEivZvDNrUHHA9g2c5mYeemeGibLPtKVgOS56q8fujoC9/Qp3dwuy5tU7K60ldxjpBLinFOSkxLYCgoykOOeiGwrp7Blbsil25liBlPgBl4wknRqzOfp2Dc0Q3Lk3TSqzGlXVIiJWH7kOg1zHmcfVhGqgqgIXqCkqblInHkWN3nZENufq5zpszrNYiaflHcCFtJ3mgSspW1cEGsBXCaNFf7FQKERC9hHvKo4v+f54+P++bkHaONsonRhpsFQl+ayUeyBrQlKHA7MSQ4UHryC4M1AVcqUagaqguD6Ntc3EHA/pSIQMWBM3GMvq2I0AoQg2dmpMxTQG1Q4EsKn36nVDPVfyynNFygsuqia457GmaIT0FqHhSXQFVCWKPom4dZBSfhH44iXWy3Pm/XPmLeAHV6Z3tzdCiN8E/iVhAYNHCNPdNy5OX85Beb22JISFkd4HHD9n+fuJCiTdFKxrEvz42oDXRgUJXTJSkRx92aIxWmNq0ie1UGNPv45oBGgxhaZWnb2bNN4+UcfqUallTcx2k/FUnO83DEaOeSw0JHOWxYZaFSOepLvWQPEDYr7CRGuSQArmRiy6KhazsRiZ+Ro0HFxVp1ryiPVKHu+UmIYklDy7Oja3wLqsYKAkae6Ls3bWBSlpatVpzmnse6nM1JgNQrB9T4o1G88OUx0oSj5/RGL78Giv5LH+8weyPVey7/tlAj+8/b1TqHLfe65BCyAiIoKmD/QxV5csDDSwWxM8vCERBZDcpkQ25+rlOm3Os1gJB+k80AMMnrN8DzB6Be3fJowgvReoA7V8Pl8tFApzl24WEbFyfHlJJfEj8/A/3XV2BdHyondUEAqEVM6pKXfqbqkqAlUBXwqYKRH84z4AlPfvgvazjVdH0bESCmogseMm8aJPpkdQ8ySmBts2xNn0wXaOPzOB0Wzw4Z/v5bN/MoNbDxACmi6QpnU55mccyguhQLnvSYZPWNQqPkf31zBMhV33pklloyiE09RtePkoqArctxnM1elMfmYg4OVxSUwT/LNtkbZsxK2FEEJh8fZ7oYqeEe8avwB8Qkr5t0KIXwb+UEp5Ugjx24QV3S/F9dqSEBrEf5DP5/dwpoLpg8A/Bz51hfuIeJdZ02swnlN5ddBjpiaZbHgcs2JkdJeU5RGvKzx5f5zBYw3qCY0/ebZOvNhgWwvcsT5B0Bfj1QWToVEfywowHB8rUCj7CrYbMBI3aRYBPbZF70wFaaqkKw08H8bjKdgdp/XELOW4iYOPOLDA954J0HSF/CNZmnNX99yfrMHWFni0T9CXNvDdVhr1gHhK4SvHfN5+vUFrDNY3wcgJizUb4+yblkzXJTtaBd8aljQWi3E+PwJ7OyXJJYP2s3XJd4YCDtdU7jQ9dBEWm4mIiLg2ntqm8aWBVmIdDp1JmBqxqVV8kukoivR2JLI5Vy3XY3OexUp4Oj4H/Id8Pv+jhH4hLZ/PP0poqP7p5RoXCoWH8vl8nFCb4THgV4G/WCzU9FyhUPj0CvT5psf1Jd8ZlZRs2NspIgfIMjNWPTM9a0kcX7A0i94qukg4HUVanneXNqdsQ82FvkXpyeEyjH/+RY4fKyGBjUMVev63D53VRho6M6kUp2IUTNPBDAJUVaJK8F2J15Gl84eToAhmKwKt5tA/OotUBfR1XfV5xhIqCAGLA2CGIdj/WgUZSBp1nwNvVLn38SgK4TSf+y6MLo7djMzCTz767vbnAiw0JC+Ph99nw5M8Oww/c0d0f4i4dYgM1FVLL/Dq4rQFnKpK91eLy3/hEm2vy5YEKBQKf5zP56cJU/J/aHHxIeBHC4XCl6/mRCLePRY8wcYHm3hZ2IiDZRzLp+YbuKbBbMygw/PoWRdndsrl+ZKON13GtX0O2wJjbo4DsyqBKWlHYdyXdFTqtFUtUnUbP6dQNw0CKUl7Ln7DJZi16S410AKJndJ54Il22kbHeUOqGBWbNs8HsnhuwNAx66ocpK9NBHxmvyRtSJpjCp/cBa1xBcNUeHVCsm9OYKMwUglIG9DVp/LahORrJ8Nb3CvDPtpQldmyZDCTQE3pTNQk7d8dw3p7DtEe58/Xr6cqBXOZBPWixaMZj807L16cMyIi4gzDZcnfHZN4ATy1TrAjJ1CEYG1WkKgu3TKSjLxdiWzOVcv12JxnsRIO0n8F/Blh+pIADi7+/xzwO1eyg0KhYAHfzufz7yy2fxr4UWAHEDlIL8C3hiSvTJyJcPz0Hs4aUY64Pra3wktj4fSGJoGpnf3Zeg5IAUKG/93KWRH1ZE3Q6i77Bz10TXDP/SbHhuun/JAcH65zbtZ9oymOUvcRi3Gpdm+ajpiK6wRomsD3A+pVn3otQNMFM1MOvS8PoA8WkUIQqA5wFSXWgUyTxq770owP2qSyKms2xhk4cqb41Kl0rQhCJ/LY/Jn5sdUZ5K4roAhBsPhjM6MB74iIiBvDJJAjTHUfAu4nrOi6kcu/XV63LQlQKBS+BHzpKvsdsUr49lDAM4c8PMtnw+FJ4oeKlLtaUBUDEIiESjKnkM6qpJs0xKxPoICa0RC+JLlQo/1OSB4+zoNjRY59fddrAAAgAElEQVQqbZiqQlu5xkkzwfGEiSsFgeuzLeaTafEZcz0Gqg7Sk8SQbKg32LY3xfp989iawkAyc7p/sUuk2UopmRp18NyArv4YMzb8p32SwZJEQXKXbrHPVHl8l4miCCwPhCIwtmVwx+o09WuUszp/V3DwDJW2lMA6XmVd4PCiE6M647Ilp/GlZys8+tI4aQPUkstsUEFfG47Gq20mjz6RwYypSNcHVUFEMjsREedhWwEDR+r8wwlJLRdDaAp/fxy2tYY29JY7k9TKPlbdZ/3WBMl0lE0XEbHKuB6b8yyW/eouFAou8BP5fP5fA3cBCvBmoVA4diXtF6MFHiMs0rSZ8GS/S5gO9fxy9/dWYfaMDwvHl5QdQfLqJSgjLsL71yqsy0psL3xYnoungggWE+yFwFPOvg5dO8D78yN0LvgoQcB0sY3Y3dvpfPEtAMbv3n7ePmMElDUVRUo8obBd92nTA+qjVWKdMXrXt/P8V+apVTxA0NGpkZ0u46kAEn2keE3n2t0fo7s/dnp+851Jjr5TxzAEW3alrmmftyRCwLZeOLiozb2t993tz0VIGYKPboLvjEDaCEfEIyIiIm4AzwI/ALwBfAb4v4UQP0qYJv83l2p4vbZkxK3BM/tdxscd1hwYJzgyTadjkxorMXj3Fqpxk1QMtrcrTAUqex7NcqJW4rnjCjXb5+6gyludrcReP8yOwydoH/f4QNsIU+19zLZleTXbTK9l42qCPsWnZUczuiKJzXlkEgquK4kpAeVJm7af3ER83SRISXtfM+MTHsm0ysYdF4/MfPW1Gq8fsWlVA9YM2hxoTjFXh4YH7dM16g2beUvw6oJBS5tOhybINATjB6pULMlXUBk57KEIGJcBj23SaJGS9oSgLRB4lmRu1mFhxqdnQVJ2oGIqvBnzsUoNsgSsSbu8/apDbLbCV1+uUUnHeOTJNp7cG0WURkQspfBCifKCS31C4sy7mNuzBHIxoU5AKqvxyAfPyF9bB+fxiw7xO1pQMwaeK5mZtDFjKi1tZ6LKZSCpvTyFX7JJ3N2G3p54F84uIuK24JptznNZseGPQqFwAjhxDU3/H+A7i/+fLxQKR5a1Y7coezoEA6WwsmVfRhDdf5efTc0XdyxlNIuKm0CV4AloSTpnrT/xZhGGq7RIiUQw8pVRfupLD/CPG/tASt63O33ePhu6ypyi4SkCI5DUGy6ZfeMYNR9jTMG5J0Nru4aUEjOuoJsqQUcCZbgCQqD3L48zc8P2JOu2JBDK2bqrEcDH7oeDvaCqsPUKKm9dAw1P8r0xievDAz3XVjV3Z5tgZ9ul25VtieVBeyL6niMiIpaFTxA6NpFS/hchxAKhBugXCUX0L8vV2pL5fL4MrC8UCrP5fL7CJaIGCoVC5mLrIlYHYrHkuzJn4TV8LASi7nKnZ2G0ajTKNgU7w4n9AfG6w9zLZVoJMHyPoZ5WXl3byYbvTLBJVdF1H2m54Hhk4tCS0zloxqkGAqdV451uhZ/apeB/bZbxwQbSDghUwfSYzeFDFp272mlpM8gAfVsv3e85S/KZQ1CzDTQheeyLA1QdH62jA9a20rAlWrOOHXh89bky011pWmKwy2sw7wnmJyzKJ2uMdGRxMiZCBNQPlrmvzaPmCqy6z3HPpDGr0CpizHY3MTBosa+1jRNGktS8w8b5ElXp8vKwSmmyQnXSonlhkn0n5un797vY0RGlk0TcvtiDZbyZBuamLGrWoFz0cOyAHjVgqAq6Knh6vbhgYdPqixOU/ymUwq69MkXrJ3fwyvcqp2s4bL87fbq4WuXbo1RfnATA2j9P+6fuRIlH0acRESvAdducp1j2KzSfz//+pdYXCoVfucz67uXt0e3BjpygIwFlR9CfiSpVLwdeIFFEmFoBoch9EIBhnp9SpeKjBA6OqmN4DeQ5myhzFkoQSpYIJLrtsyYr+MSj5ztGTzGZjlG0VZKOx1jaxPKL2CUXteHiOBrFI2VmZBKr5lOvBjiOpLijC1kKjXp/Z+eyfRaKGv2eLoiiwB1Xpft81XzxqOTYQviOf7wIn9qz/N/FgVnJF49KAinZkRN8bHPkJI2IiLhueoGRUzNSys8DnxfhzaWPMA3qglyHLfkpoLJkOtKFuYn58S2CL7zu4XelaR2ahZhKo9VENJm0FBscECZWJaAxbONPVOg/OY+mwHBHE8fTzcw5gtra9aRM6LWPYzkqftKktiXHfGDi6xplqZEzwdMFBxYE73k4yfR3p1gQgA1TI4LXvivItRvcf3ecbE8cNXVp3dHjRcBU8RseqYU6/kiZ1haDB8am+LqrUNUNDs85vK0IFmJpZioxlBq87Au2nZglUXSIS8m8rnHc0DB1wURM408mNWIpjRFNBT9AafiMCoWvtHUx1BujbEkcHzoqNup8A8uQnPBhwjFZXyoiXJ/MeBnrjRl4avlsxIiImwnrwDzzf32MwPLRczHafvlOMk0ab75UIwgk29oNProjuGgavX2yfHraLzmUh2qnnaMAE8M2pqnwTqGC8fwUXZpHMq0R1D38oh05SCMiVoZrtjnPZSWu0DvPmdeBrYAKvLkCx4tYJJcQ5KLI0WXh++OSbw5KdAU+thnSNZu3X64QBJJNdyTPS6sqCZO5hAmAMOMYnn3W+jVbk6SqdarJBEJK+t3a5TthaMRqHp6q0NJwqLaZJCbLKI6PVBX8uE6brlCdq6ElNRQhqQsVHuwP+1GTSCmx9s8jbZ/4zlaUSIDypmNyyU9lzpJ4gURb5gGQ74/L0xqlB2YlT6wRNMcu0yhi1SGlRAbRgEbEqmEA6AKmz1nesrjuUg+ka7IlC4XCZ5dM/9lV9DViFXLXzgQdWYXR9a0c7tYYnPaptycxkjrGdJnOrOCED1NjNvd+b4DO2QoakoRtM7iri4znUe5s43AuhnVfjvaDRYpHLcTJIubGZu7qUjhYhdji21AuLpBHSmxOeRzxVYZqKq7nM3rSpnykQtebdQzbRXn/WtY93k48eeGfsONLjqNT0VS6cHgj10bRE7SXKpD00JWAWUVloCVFQ9dQfYnuSQ4FGj2OpMeykcDW8XnshIbZEWegBHrdozHvMZuIYbgBIpAEOkxVHSqtBvO+iul4aK6PFkhmUZlTDOayGnNrBHcNTdHbobA+EdUYibh9qe+bpfbqNNL2UeIamSf7yHXG6eg1EQISKZXZKfeiDlJzfQb7ROgkVTMGyd4ExhEXxw6vq2yzxv7XKnhugOxMM/32JP2bVMzOBFpbGFlamneZnXJpzulnpeRHRERcM9djc57FSmiQPn7usnw+HyPUAnhhuY8XEbHcuH7oHJUyHIn/5iDsnKwTBKED6dg7NdZuTqDpZ5wQVkeW+VKAHoCjCDq6z3agWobO5loFe34BDVAuIGRquZJvDkkqDjzQLeiUHpOuj0KYlr8uEWB0JRAzdWSTSUcS5OcPkJ6so6qCTOd2etpVxqbDAlH9/QblZ4apvRLeJ+r7Zsn9/LabMjLw1Hcya8FdHZdPF7+V2NkGLy4WCNvWKpbdOQphEbHRxZgrQxVEg9s3H3NTDm+8VMZzJVt2Jlm/NRoti3jXWRTmPo8U0LhUw+WwJfP5/Elgb6FQmDtneRPwRqFQWH8l+4l4d+leE6N7TYxtu5K889ICRwYcvLrLQtkn16hRVFyaLZetkwuojkctYZJrOOypVRjRTIyEx6YOnbYTPr6v0Npjkp4vs71W4eA49JgK+fUmd+VgT7vELSVYn/YZrCrEVIkSU3DqHo7jUbcbVMse3j+NMyMMHn6qBeUCz+QXRwK0aYtE3ac652K5CgnXZyqdQfMlNRFmCTXVbOaSgnpMxwkCdM8n8AJqhoarqlR0hRbLRi0LJj2dmbiB8Hw2TpWxdBXD9fEUONrehOsruIAqBHMxnYlkDFtVsIXKiVSCLsvDdAK8BYm+vfnGf5EREauEoOYi7fBdSToB3oJDdmOGVHrRfyIEmaaLG8KpB7tQm0z8oh1qkKZ17n28ieETFrGEytpNcUYHw0ecvbaZl/QU8ZxOYmsz/9wXUHF5+dvF8L1SCPY+lKa1y7wp388iIlYR12xznssNeQ0uFAqNfD7/fwHPAP/lRhxzNWN7kq8PSGbqoXZovjO6Ia4mFAGaAu5iIXpdBd048x1puoJyTgq92p7iYMxAylD84q5296z1sSaD2t4u9MEyrqEQe+R8vcpvDEjengmv66EydM9UyExY2JpK0nFpborR4jk4SQVNeiRnysz4GsVcC4qQNL05zb3/bi8T3xxHNRU639/D7H89dHr/7lgNaXmIxM03UvmdUclrk+FnM1iGzgS0J2+P6+Z9axU2NkncADY2A74PLx6GhRrcvR56c9d9jKfXC1QhqbrwSK8gpt0en+2txOG3anhOGL1w5O0avetiF5QDiYhYaYQQp9LjJfC7Qoj6ktUqcA9hZdGr4hpsybVcOGLAJEzFirhJkFIy+Hv7sf5pjM5AMHZXH2ZHhkyzxuYTJfSpGqobYDUk8VqNRsmh5ZkBWjZlKekGd+xO8NarVbwZi3hCoTctyes229MaAkiOWVgDAS8cUrn3sSZafng9a59bwCsqFOuSoOayXtooVkAAyIROverjuRLjArrgJ96pUSoGWAh6Sw1GknEUKWm2XCQKCdvlviNjFJMm7YbGQHsz8UDSVakTKAqj6ThTmQQCSUwI1LiOV1eoCwXf0DC8Gu11m6TtMp8wmbRc5hQFPQhI1W2qisKL3S1kHI+5mIEnBJlknONrOljwfPhSkZ/6RPyGf48REauBZL6d+r45gpqL1hbH6E6Q6TPZeW+GhVmXti6D5tyZd6Wg4VH88iDeVJ347hzpR7qJ72g5a5+prMb2PWdk03bek2b/axX2nXA5EEthegpd5TBja6vrng66sQfKHH9tGC+n0PLjGzH6Ly69FhERcT4rYXPeyDihHKEH97bnO6OSt6bDG+NEDXrT0HmbOHtuBlQl1GD8x0EwVfjIRkFqXZoDr1fD6KxdyfNSWDt3p8k8XyfwQdEEPbuzZ61PZg3WPN7JsS86qAmNhz9yvtRucUlWvutL0rMV2sZLeIqC4fmo63Ok72rFn2ugZAy8ljj1WB198SE7m0iiNZn0/ei60/sx1qTw5sJBE70jjrhJQwPLSz4bKSUVV9D+7nXnhrOuacnv7bl34HuLju8Dw/ArT0Pq+l505iw4XhRYHvSnJWuz0f3oZkNd4gZSBOcN4kRE3EBOpccLYBuwtGqhQ1hh9Peucd+XtSXz+fwPLZl9Op/Pl5bMq8B7CdOtIm4SZt5coPr5oyTmG5i+pLvicOxHdmPGFXACGhWPBU0nV66gSUmcBp6i4LVodFTnCdZ0U3dAUxUaDcmAJ3DrKl3UcQrT1OIaIt/BySN1Ro5bPPRkCxs+kmDoy7Noo0X6dBdDBkzEEpidOuqmFlo7jIsOQkkJJUVlBo2xnhym69Fed5hOmNwxOssdA5N0zRZ5Ztd6JloyPHp4FJGM4yqC8XScqXQcV1UQQMzz8aqCiq7iqgquqjKeTdA9voAqoWzqaEGA6vnsGpyiZ75Ma8Xi2TvXMZrL4imCmOej+wFlQ6dvocbogHVjv8CIiFVE/K4czR9bjzNUJbY5S2xLGFHdszZGz9rz9aVqn99P8J0BgtYWylN1GsdLaGmD1MNd6J0Xztbp6DExTIXhWgm9phAEYVq9rqg0t+koisBreDhDFbJNksDyKH9rlNzPblvRc4+IuAVZdptzJYo0/do5iwShHsBPAF+/TFudUFz1vYVC4cBy9221UFsSXCilpO5GzoiVpmRLpmqSNRmBeQXRcVtaBFtalm6nce/jTRfd/sk1Ks+ZDZRaAxJJ3ntObIpTcph6q4K5NnwID3xjko4dTaf1agxT4f5uwVgV/ECyrVUwoUh6J+bQ/ADL1Kl3rCHWbOC3xVHiGqn39ZGeUqkfLqEkNTo+en6xoOyH1qJ3JZG2T2JP7qZN37gjB391OPwed7cJ1qyS2sPzluT5EYmqwON9gsw1VJi/aqaKZ6YdD4q163aQfmtIUndDR/vzI7CnQ96Yc4lYNnbk07z1chnXCVPsNT3ykEa8O0gpHwcQQvwp8GkpZfkyTc7jemxJ4G9PdYUwJX8pLjAI/PrV9ini3cHaP0f5r4+hzlsIKRGBT6Jm0RaXmJOzdAZVyjUbP2EgJMTqDoqU9NqzOK0KLZ7LqweSvNjSQq9TpKdUI95qoLkBfOEI2ZkqotTg5FvzzG/qoqwKvvb2PImYIO54tJYdhswYbYGPKyX1SZu9wTCtaivSzyDU8++123o0vjkR4AqJrWsofsB4JoESBHTHNCoJkw5D48e+f4iFbJLBrhzvrItRV1Xm4wYVUyfm+jiqQjWhoQSSuYSBpygIIRhsTtJTtYg3PObiBpqU9M9V2D04iRQCISWbJuaZasmAAFVKjCBgIWFi6RrNI1d9SUZE3DIIIci85wqTCA6Nov/jK9jvlPFLAeVML/EHejG6ktgni3R8MIeIG7Cx67ymZkyh25Ds8FwGXZWNGZWHegSmpnPfe5uYHrLwj/hktND+FpHdFhFx1SyHzXkuKxFO9qlz5gNgBvhT4Hcv1bBQKLj5fN7lFq86en+34NgC1F3JhibB2uzl20RcO+/MBvzL70oqTvh5/+F7BfFlfgjFXz3Gp7/+XWL1OvVUCm3XE9B7JpKzUfOxvTPbl6sBw8ctDrxRBWDb7iTbNif49B6oe4KOBHyzVsHWFBACIWA7Ddp+aSfuZB0tF1ZRfeDn1jJwpE4yrbBl5/lpGUIRJPfe/LGWxxZgczPYviCmhYWLeldBFsrnDklmrfB2NVOHn995A5yKu9bC8ckwRKWzGTou7ri/UrQll4MiBFGNn5uPdFbjoQ+0XH7DiIgbhJTy40vnhRD9hNGfh6SUl7PzrseWVADy+fwAoQbp7NX0O2L14M5YLHzhOE0HDmC7ZbyGgmcYZMwadnWW6ZEGacXDT/pUXRPd84g5Lp6mEms4dFer+O/t46CRZWdhDLXUQLM9YhkF3XHIDM9jVmy8QKKXbFqOTlE1TBZ0lbmETnM8QJupUEwoWHZYIBMJXbZDKlWkcbREfNv5ep735yT/u6FhKSogaRgaeiDRAohJgWcajHa0MptNEXNcqskYI9kER9qy1AwNpCTheiRcD9MLaKrbxFyf4aYEUgh8RWF/WxMbZssUYwauqpBAIiRovo+QkPYDDN/HUTUkAktTCRCUDJ1k4Jz/YUdERJzPyUl8I0ZptIoMBEG9ijtZx+hKYr7yNpRN0BV4aBs8seuspomUyu77MrScsEikFLbuiqMtBulkW3SyLTp1sYbKc2MoSZ3sU/3vxhlGRNwSXKfNeRYrUaRp3eW3uiR/APzLfD7/8UKh4F1265uQzqTgV++GmitoMrlpo/puFIEvqVZ84gkF3bh6x+bfHA6dowAnipLvjAieXObSDMXnjrBmagqBJKhWmXnuGDx15lJIdcRo3Z5h7mAZoSv0P9rGkf210MkFHN0fFn7KmIKMGbbZtivFwOFZHFWhWfFZs6cZJaZhrj0TPjk1ZjM35VAtq/Rt8C8pKn4z48nQ/jjl1/ZWQQFWKSXzSySf525Uxtoda6AtG0aOrmsH/fq/86fWCf7uGFTdMBI2aUT3pIiIiGtDCPFjQIuU8o+WLPsj4BOLs4eEEB+QUo5dbB/LYEsuyz4i3l2ceZu3Rmwaso2ezS5dw7NUm7IoG3SUdyYQXgLTt/EbCk2zVXQpEVKiygA/aWCsS1NLmRgOpAIPl1AoXitZNA/OEXM9qLtoqsCUPtWGj2sqyAAypTLxcpWNMyfpiTXzVstmRADJeoORWRutYpN9nwUXcJDamoIGJBwXR1FASnwBjqFTSsZZSFoEAg725vCDgFzNxgEsXcVXBEJCQ9fwBDxydIItE3NUkjFe3NzDieYUnXUb3Qs42J6ltWaxru5iej4HN/aQLdVwDRVF09G9AKFLLE3BBzpKVSpxnWMbosG0iIhzqVd93nq5TMMK2LAtQf/GOKzvxJ59K9QuUhVEexpZ8bBtjwk1iWkl2ajUiB0YOe0g9eYaeDMWel+Kzj6Tzj7zwgcs1UgcPEgiZcNjOyAX6QJHRFwty2Fznstq9KY8DDwKjOXz+XeA2tKVhULhB96VXi0zhiowLlQ6IOIsPFfy/321wsF5Sbsp+eWnU7Q0X93Ptjl2pqiZAFpW4PmTTQi+vHUnY5km1hVn+ejZRexRVMGjv7iWiUELM6HR3mMy9bU5vMVB/As5frt/+268yTr2YIXUQ100fWTtWevLCx4jJ0KvnG35HD9QY8+Dt2Y48sM9gqFymNK+q311pNgLIch3Sl6dCOfvOT+7ZuXoaLqqyNE3piTfHZWkDfihTWLxmjhDLiH4xK7IKRoREbEsfAr4i1MzQogngE8Cvw0cAn5ncfoXV7oj+Xy+GXgK6AeMpesKhcK/WenjR4SDia9PQdmR3NV+/vPnUrxx3ONQkMXXVCbbY+zOaDRasii+zwmzE9fySfsum2dm0XMZSkWBbatgKKSSUD5WZuKghXjQZCFQaHFd+so1Og2P9ukqZUViK4IFM85MPI4SgOF6OIaG5rrktBqxuELCmme8WCIwTFTHQ1UEbnsC+0SJ9KPna8pv2WDQUSliOwG5hosuYTSbpGaqlE2Ngz1t6L7PVNKkrqn0LFRwCCCQoIYWqwgk7aU6u06Ok6s2aB2cxNIVoJvmhoMA1pTqYdSsL1mIG6BrvLW+E0vXCABNSnRf4uqC460pxrJxhK7yoU3RC0hExLkc3lelOBfq4B14o0pbl0F8Wy92Ty9uRuK5CkIqdP6rPbw5rVKzA4TlUPJ0HrwzfPFzhivM/flRpBewoBmYT6+je1OSwJcMTnrMGzrrWhX6MgK+WoBjiy8Rf/MS/PoPQMy4WPciIiIuzLLbnMviIM3n879/+a1CCoXCr1xmk1ngi9fXo4hbhTdO2Lw4IwDBQh3+4Q2bH3xIZd80pA24q/3yEbif2AnzjdDB9miv4J6u5dd4GXryHl79+hxGw2asvZ0HnsjhBZKpGjSZkDQEmq7QuzFxur+7H8hw6I0qEti2+/yaE1pzjPWfe99Fj6mcY9+qt3BedEtc8Ct7BF4g0ZTVc54fXK+ws02iCuhKrZ5+LaXiSL56QhJISbEB3xiAf7ZtdfY1IiLilmAL8PKS+Y8A35RS/g6AEKIB/OG5jZbZliSfz98HfA2wgTZgjFDH1CbUIY0cpDeAZ4clL4yGg9RvTsEv38UVacEDTIy5NOIGbSdnEa7kjTVbWb8rw6yMI07WaVFs6koTY10afcImkdGwezP4vWmsl8coLwRYasC2N0cxAp/NC1N0ZjxkGaZPqAQNyWRrlqnWJiqZOKlKg2y1hmvorBMV9nSXmX2rDmWH3myZYlMG1VCI9afp6ddRLlL4Mle2+KGhIV6Kt9Dk+GTqDWxNwdGSvN3RRNLzEYGkFDdoKde58+QEuufTUbM50NuGh0QJAtrKNTTfRwrwhYJhubTUbRACI/DpqjQoGyqmL2myHKQQBAImkzFmkzHiDZvOus14Jk49ZmCpCoamcKwW2QAREaMDDQ7tq6Jpgl33ZfB9CY6PUmoQZMzTlebpbSX+ZBy/2MDoTZN5Ty/2F2cQu9fiH5+hrAr46HYA6u/MY9c8ZlyVgZok9r0FDh6wsAN4YwakrhDb2cxP71ZZV7MJnAC/aKMkNVTHixykERFXzzXZnJdiuSJI77z8JsAVaIsWCoWPX26biNsHX1MJayqEOKrCf9svKdnhT2m+IXhizaUNvaSh8G8fur5+VEoe7xQq+J5ky84UbV1nP8Delk0cyCXxFx14b7sGx9+WTNYkpib46R1wsgjPjUiSuuR/2Crobta5773np2ZdKamMxtbdKQaPWiRSKlt2Ji/f6CZnNTlHT9GbXn19WoofQLBEesX138XORERE3A6kgIUl8w8An18yfwDovEC7ZbMlF/kPwF8CnwbKwHsIs5L+ivOLN0WsECOVM9MVR1K0BR1X+PbR1W+Q+qMX6R6fRCCZLXfRePqDxGoB7kAdY6KMNW0zayhoMsDI9zK0IFCPl0li4AcghaR9aoFYvIKvWRyXTbRIl3JLGnO2jmUYeEKhe7JIU7mCKVxaRI3yHT28o3TSrLrUc03EYiZ3qnWyv7EHdaRCslkn+8ELawaqWYNt0mawWKURj7GQTdJRs5lKJ0ARlGIGccelf6bE+/YdR/clvq6xfaqI0DSaKnUOdjSxkIhhaSqKIamrCkO5NM2lKiaCStykoSmUTR3heMQ9n7qmYviSDQtVErbLE/uOk7YcGrrKHz+xBytpYKiCqnvBbkdE3Db4nuRAoUIQSDwHDr5RYccWE/vPDuBNW2TbDNRHUshkmuQDncgXJtDb46Qf7wGgd12MY9+uYM9AV8Kn+I9jZD+0hhMTAbVjFuOOiun7GO0GU65CTdcIpAJOgF9yOFmK0b93C9aXjiAtF7evm+Ssh7kKMuQiIm4yrtXmvCjL4iAtFAqPL8d+lpLP5/PABuCrhUKhls/nk4B9q+qSRlyYves17hrSOTwZ0JoSPLLD4K+Pnnk3GrpBhTj3v1qhNB9alG++VOKJj+ZQlkRsBt+fIFNN4KgqpudTenGe8mN9ANheGD1xaC7ctmzDNwfhZ+64fsfaui0J1m1JXPd+Im5dmmKCh3oF3xuVJHTBey8zoBARERFxnYwCO4BhIUSG0PG5NOKzFaie22gFbMmdwM8VCgWZz+d9wCwUCifz+fxvAZ8jdJ5GrDBbWgSDpdBuy8UFLbErb7v3viRHGwsoGiiKQkttgamSTWtSoz3tM+ZKSuk4BpJjmNizKl7CQLQrtDc8NMejHjORMZdtxSHSWQ2Y4fianaiVgIav4Mc0Mo0GGj4xrUGLW4N2n0RQ5dCd9zMdtNE+GNb5UvvTrP1IP+Iyg7Vq1uStD9/B9952WVu2qBcTGjEAACAASURBVJoGVV3FF6B5AZoIMD2fjlKV3YPTWKbGVHOahqaRcDz6ShYbJ+YZzib43pY+TN+jFI9TSsXomi+zY3SOmZ4co21pWi2XhZjOtDBobbjkag3StkfPTJFcxcLwfAxfo61cZyhmoOLxgTWrUWEtIuLGcu5Imz5ZoatWxSnV8Q7XGPrpeVp+YjOtP72FRmeaiVEbqy1JMpBs35NGe+YEQbNHWg8Y/voYCzLJhGPQekcbrd8awlFUEgemyWwGoyvFZDXA0XXcike8YlMjSfXuPML3kaaJcnAec33kIY2IuEquyea8FKvuCZnP5zuALwP3EN67NgEngf8INAgjASJuEzRF8OvvMSnakvRi4Zhc/Ezl8E1N1+bsKUxKji1I+jOCB3suvw/PPfMY9f3TtZVO03d8mg+Ou9iaStz26N2WZFz0c6poWpMZVqI/NR+5qCJuJE+sUXisL5QCiIrCRURErDBfAH5fCPG7wJPABGenP+WBwzegH0tLdU8Bawj1qKrA+cKRESvC/d2CXFyhbMP2VtCvQg4o1hSj99Feis8NAZL01k7WbWqQeuEt6ofnqMbXUy8JPEXg6CrldALXEyiGoLS2k9ZilXpzkhZ3AU1NoOChpHU639fJwnGH0a5mij3NdHx/ELUuyTYcjKyGlVBobOshE4PYuiSoLkZXknW/uB7Z8BAJ/ZL9Hq1IKgmT6WaD2XQC3ZeEYlGSQBWkrAY7h6Z46MAIWuCTrXusmS4x0tFEwvGZak1T0xQUQ+dwbysNQydXsTBdH1WCq8BwLsNoLkPaahB3A+4cnqK/5oIQeKpCe6XB2rkyIPj6rnUEisDwA2Key2/cG6XxRty+BIHk0L4qVtWnXPLoXRejqUXj0DdGUfbNo1kuQcUhaPhMfXeSN/wkJ2agpV0jW6whJWzckaSlO4YzWKE45zFfg3I5YGbSJZ6O0dyZIB1TSP3/7L13lBzXfef7uRW6q3OanGeAQQZIkAWSEpPEJNFUcpBtZclaWZafV97n9bH19nn32Xrr9dvj53W298le71qyLFuysmWJskSKYiaLAEjkOBjMDCaHzt3VVXXfHzUk0iAMMMMA1OccHHSlW3e6MYVvf+8vpFQ6Jyao7Zuka6GBHRbs27KdF08kqHarDCoamubbMXpbEPASEHAFrLjmXBWD1DTNdcDPsHRR/F+4xOV/iC9kc8DJM/Z/Bb/DfcA1xp5pyUIdtjb50W5n4knJVw9L9s9CS1TywU2CX9gq2DsjSIRgY275Zs+Recnf7PGYq0lSYUEipLCt+eLjrNsW48VnCngerNsSRT2nflZNQku+gqMoaJ6HNBL85KDgpWlojcI9PX7UxKMjENP9ruEBAa8mr8fyBAEBAdck/zfQBfwBMAF8UEp5ZnGP9+HXBr0oV6klAXYCO4DDwI+A/7y4CP9B4KXLuD5ghRjMXPn/P5k//CmMz1t4NY/Qu7cR+sLDoENsY4L1c0UKA4PMzTrY6SixTJjGdA1luoZSquGENDxdpdDVTnRuHEO4iMF2bvo369ENjaf/8BDHHh0n3xonWgtxMhemd4MgH42iuhHWPHqIiOugb8oQosbk3xxiesrBuauHje9oI9eytNFYsiWHZjyydYeiUHBUBSEh5Lj05BdoLlW48+gIbYUyAjAaHoVYmHJIp3lmnhMtKcqZODVNYcexU4zmEkynEyRrdRxVYbIpiVAFqutR1zQ683nuPHyS450dSEUgFYVi3EAikNJjuCnJdCqGQCIVhVQo0AMB1ymex8w391N9foZkfzdGe4JYQuX4U3PUxj209iZ6942gagI7orNnRHDKKtPQNfLzDdZuEhTzDrsmJZU39bMmPoY7VKWWTqLrAiOiMDVZouvUEB3tOm62C4p5WvLjZAsOBVehnB3mRHwTC04IeW8v0WIFvS1KzGx5rd+dgIA3IiuiOc9kxQ1S0zQfwm+ytAu4GXgeP1U+DDx+GUPcC9xrWda8n2X/CsfwRXLANcSTY5J/PeEB8Oy44Fe2g3GG+Xh4Dv5mj8fJot/sqCMmePegekUdwz0pUYRg37Rk/6zEkzBeluyckJc0SNu6wjS/pwnPk0t2nD8ldaIhHUVKaprKaEVlW7M4a9wd7bCjPRClAQEBAQHXLlLKKvDhixy/ZCr9CmhJgP8TSCy+/i3g8/gL7YeBoN79GwQRCRP95O3+hufBYhaEmgyxdiBO8/u3MD/T4MSRKof3lEkMRCk/YxO1K5Rrksh8iehb+hkq30rPwZOkZsPUn57AbYmg7JpEKTs8n2uiuDlNs1Pn7S0u61KSzMk5CuUacwUPSrNEXYfqQA7HlrgvTPJiLso9725acs6GBm7VI2x7uMKjqqnUQxoSDU163HBqjMmBHNbmNaw9NkHf+Dz1sEbV0KkKwVRTnHS+SmaywPHmOGgKYdelGDeoRcIoCyWailXyRgjH0NCAZ9Z0crgtR0+hSqZaJ2fbTGUNUF1mUhGkIlCRbOnWSIYDLRpwnfKjfUS++mPaT5bo+OGPKawdoLh5LTLkm5ON9U1Iu4xatZkXYSqREI4i8FyJ60jqNcmQHualox6gsXOgjw/d6zL1WJ5SwcF1PB584WESc5OoRRVjXYiFvmaYBlUXoKk0QiEURaCHFSKDKWYWYqiKoMeR5wXgBAQEXJyV0JznshoRpJ8FfseyrN8zTbMIfAg4BXwBePoyro9wdlrUyzTjp9gHXEO8XJcK/BX3maqgK3H6+M5Jj/LOGW4YmSOfjvL9bCfvHlSxXYmmgHIZ6cKFuuQL+yUzVdjSJEnoHjNVKNkQ0SCqX16/B1UTqBdIjk+2RSiPlvCEQJGSjq7IZY0ZEBAQEBAQcB5XqyWxLMs64/U08OBqTPRaouFK6i7EVznCsPLSLPVDC+jdMeK3Xbp3wtx0g5PHqkSiCmvfeQvqwztB1+CdJqmsTiqr07cuym33pJketzl+dAI551EVkrIHqiYpHSxRn3Cpe3nmvniEyFs7sSsu87pOUVHpf3GUbRGbpqpONgMIQaEOcrKCEtOoS6i3u6iqgmdouM6FteNs3qXrpQluHJrmWHOafDzCTCrCbCLKptE8/TWF5Hqdybkybe4IHV6eeiHDoVgLL2zuZrw1waENrdSloFCX5CpVhCPRXZfbJiap2QrJcpWpmEFZFUx3psknDN7x1AE02+VES5ptB0c43pUlhMsdo5N8sydHawS2Le3pBgRcHzyyh/jIOOL4AnKhDnlBm1OmdNfdNGI6Ws2m9af6cFSV+RdmyZaLzM/raJEQnRuz3P0TWZ56cZI7vv88AIfuMJGbernzwSwzkzaHvnqU+OQ0UhEoGmij47i//FZqIYfYzBROXyvZdVtoS4bo69HZ+81TFGugt8dYmGuw/c2p1/gNCggIWA2DdD2nO0c1gKhlWTXTND+LH9763y5x/Y+BjwL/YXFbmqapAr8J/HDlp/vGoH6iQPFHp1AiGqkHe1CT10b9oDVpwZF5X2SmwoLmc3xFcapI89EZxmIRYrM1onsmeXh9B0+fkkQ0wfs3Qnfy4kL+iTHJdMW/x55pWJsWtEQlcd1f5Q+vwGpd4qFeds0I4sUahUyETQ92XvWYK0Gt6nJwdxmnIVm7OUo6d/G6WQEBAQEBAa8DrlZLvoJpmmuAjYub+y3LOr6iM71GOFmQ/P0BSc2RbG8VvHvt+dkyK0H9RIGFr/kfQXXfHEpEI3rDhV272Smbf/mHaZCSTLOOtzHHxt/4ySXPNSIqnZ0hvBaFoQMSTZGkHupDxFSyok7K85ttSscj0p9ACanUFY16JES2VKNS92hEwY5q5B7sQtlfxFNU0BWYr5Gq2eR7MjS2t7F5e/yCcz74lZPcfuAEHUemSKzr5vHNPeSqDeKyzJaFBaYiBo/MxfjpPT8mVq+g6A4DlWFmk+28KeLQnl/geUOhrKhUHAXDkXQ36pSyBrdvzCG+cBCt7nKklONwMgKGRsdombZCBRyPlnwZo94gWbIphXVE3UNpeFQkPHLE44k2hztuDOodBlyHhFSEAM0BERKkUipyqox5a4ziQ01kYoJILoyU0Pjt7zNxwKWVMiHVZsuaJpqlzS1/+R0WSg3KPWm2P/8Cyff3oioKHT0G9c0J6pkUkbl5tGKdIydUDlsVIl3rGJApInXB1oV5su9cx9TnDjCzz+897ZUazEeyr/GbExAQAKtjkBaBl3tUjgNrgb2L98pcxvW/ATxmmuYO/FSqP8DvTJUCbl/x2b5GnCr5EY1rUhC7xEq9bLjMfekosu4ubnvkPrhu1edYbUh+cFJSacAdnYLOxMpHFNzWIUiHFRYWi/efa1YeHHMYzvkd/cqGjrIgiJ3yzc6qI3nkJHykswyP7vU7H711K6RjZ42hnaPxcxHBbe2Sgg1xHdpil/dzuUUb6Ui0TPi8Y1pUxe2OUy6GkKkwQn999D/b81yRmQk/IHthtsE978qhLKNBQkBAQMCrydG95fP2Bd10rkuuVktimmYO+B/AuwBvcbcwTfOfgV+wLGv2aiZommYU+J/4JQAc4Ncty/rnJc57C/Av+Kn9AHXLsm69mnuvBo+N+OYowK5JyZvaJS2XqY+WgzNbP3t75sLJYZ4refbRBeZnfB3TaEhau87XYGdiDxVQ56v0ZCRCEaQ6PDLv6WC6vsDCt/MIIHZrK4nbWukP6Qz9/gHuK07hhBVS1Rp6STIyDulSg9aNCUYlMFRA0wT1lIEo2bStidLVb1xwDqmpItH5IlG7wcBMnvDBYaabYky055hqyqKVG7RUG+ieQ0NTSReruIpHpFAj9uQQyUyM+Y46RVWhNRmlkIzQonjcr1f5+N0xntkZZ3z3PC3SQTQJErUyqWqFVtXBKdoki1We2tBNplInJMFOqfSfGsU2DLxkku/t8QKDNOD65L4bYK6EVxtHzOVxNZ0pmeL4IZWOuRLtd2YQikAAnZtSHJ6soEoFV0iqDYXZzx8i5TgYwsGbztN8UwpVEUyO1qhOlmm7rZ3I7zxE5Y9+xHxVZV/fLbgHF4hpDoVynaQyz8KBOiOegJE66ZDCgi1w8w2aOy7+bAtYHc7VnYHmDFgNF+dZ4A5gP/4q/x+YpnkD8JNcRlqUZVn7TdPcCvwyUMcXyF8B/tyyrPFVmO+rzoFZyZcPSaSUpA3BJ7dBRL+wCPVs7xVzFMArLlWBYPlIKXEmqyhRbcmI1G8f82t1ApzIw6+Zy+s+erlsuEijpXpHAttoYNQbuIpCoTuFkNCYryNCCqFsCL70OEzl/Qum8vDJt501xp2dgskyTJThhha4v1dQmnE4MmnT06FjtqqXnGNl1zQL3x4GTxJ/cxvJB7rPOv6O6Qn2TZYYCxn0n5rjbQWF18MjtlrxXnndsD0cRxIKDNJrjsNzkpGiZG1a0JsKPt+ANy7l9256racQ8PrgqrTkIn+Nb6zeuTgewK3AXwJ/BfzUVc7x14GCZVlrTdMcBB43TXOtZVmlJc7db1mWucT+1w2RM74RKEJgrNI6r7EuRSkZwi3YKIZKZMuFo6YaDQlSEomqVCsuDVvS3R/BdSVOQxI2FKSUeB6oi9pGzYSpvjSHV/GjRasvzZJ971qaf3kL2Q+tBylRkyGEEDQdOURLskamXGakNUsk20pN8ShN1Bn/f19EuB5tXXG8DXGKikatAorjMn68SveayAWbNG3bFuf4wx52TCVTL1N3wpSyKXJ1m1pYJ1n3MByXQy0DrJs+iio9jjb1EkJC3SGxUKUlWoF4GL1mkwgJ1jtVMvvK7H5siva5EqlkhC2VAlY+SXyqRA8NbigXqdVK/NOmQZ5b14MrJOlGmU/sfYJssY1kw2GutQOZ7l/5DzYg4I3AmzdAOkbo/jnyRxuMDjuMtzdRfG6G+UID8eUD9P/SBmJmC5GHtuG+uI+xaQUSEXhmlrI1x0xjkHRjgRvFLKGfvpXhfUUKf/IDjNk5RjJx+v7LQ5TfpVEZLuPNaqilOkqlSnJqGM+ZoW6rGA+XONh1I2siLs2GIHZjikHzwlHpAatHoDsDzmU15M+vAS//hv82foH8n8ZfOf+1yxnAsqwJ4D+twtwAME3zg/iRqpuAf2dZ1p+t1r2WYv+sb44CLNQkYyXB2ovEQ6gxnajZTMWaRqiC+J1X0KHoHKSUzH/pCLXDeYQqyPzsGoz1Z09i/oxF/qojqToC/dJe4orymTtUvj6UY3zBRY8ofPHtKnz/BE9Mq8RxuTudgrkzvovMnf+9JKILPrT5tGk0P9MgfqLIdilhtMbshELLJVbtSo+Pg+d/ZqWnJoi/pQMldPrNiMxU+PTQUTxAAZTZ2NIDvcoMrI+wxyqBlHSviRAKr07KXMBrx+E5yd8f8I3wJ8fgE9ugPR6YpNcjhXmHRsMj06SjKMG/gYA3NFetJYG34Tf9PNNQfdI0zU8CP1iBOf4c8BEAy7KOmKZp4dc5/coKjL3iFG3JkXloikDPEqWJ3tYvsD3I1+H2TrFqjXzURIjmT22mMV5BazZQExcuGRU2FFomFjAOz1GLG3R/YAAjqvDot2Zp2B7pnE6l7GLXPPrWR9l4YxytySDUn/ADAOKnAwCEEGjnBANUjs0RjUUohg02agWqt/ZRHS2TGT6J4nkgQAkrJO/uoPzULFQaNAazoCkXfcZ2PNhF6a/3cbiik67OM0mYsuIQSSjIiQaaIoi5LlPJDo50dGHYdbYenSZTsbFDISZzaTpKVZrnC+TbUhh2iEyTTttcAW+2TmmhQW26wb0TNdYencIVgr64R1WoTGkJqvEYzZUqhYhOq1Lnhi6VNS/sx6k6TKYXEDc0AUEx0oDrlE3daJu6yQGnnitSe2wKZ76OM1XFrs4x9ZnvYG/OUbx1E+76blr6PCIvTeA9M8lQOErIsZkJt3BgfT/NaozK8ycwZufwPLDHCox/dS+ZTd2Ev7aHLTPzVLw49f5W5PAUZWrQlkTDI9UfRutJMThoYGzOIi6jr0ZAQMDqs+IG6Zm1nSzLqgCfWu4Ypmm2L173sqW/H/jvlmWdWpFJwm7g54HPrNB4y6IjLtgz7ZttIVXQdBn9fNLv6CP+pjZEWEWNX30dSWe6Su2wH3UpXUn5mcnzDNI1KfjWUbBdyb29qyeWL0Y6orHn4x6HZhR6UwrRUp2pU3OsXzwe2mfDLYPw1EF/xy2DS45TOzhPY7KKsSFNPg+4HqLuIMMa+TnnkgapkgjBnO8YKxENcU7efvTmJkpPjOPmbbScQXTr66OOTNdAhGxrCNeRJFKvj7T/gJVlpHi6UYQn/QWX9mAR+rrjxOEKB3b5C0StnWFuuiMo9B/w6iOEuOyoTCnl1y50bCW0JDANnF+zASrAVaXXL9IDDJ+xfRLovsC560zT3IlfT/UvLMv62wsNaprmLwK/CNDWdukGRpdDpSH53IuSoi0RQvAz62Bz09maLhESvH/jq6PzlIhGeCB5yfNqh+Zpn8+TyggUWSW5e5Rje6M0olFQBPt3Fsm2hDAiCicOVegeMIgnNZJv7aS6dw6A6I3nG4FepcHcd0/y/G4DN+ygpFTSXXG23ZOhOhrCHRqjNgMIgZoOk7i7g+6eBPUfTOO2xxnYGCXTdGEtbk9WsDb08lJOQbcdPM1mzEmSnfQwbElSkaTDkrVeldqGVqYmQxSkoOq5NGqgeR6JWp3uSonZZBq328DzIB4GL6pRGXOpS4FXqdHcqpEsVtCnXGxdZ6EpTe9MnoamInXB+rd10/XjA0xKh7qmkE+kiE0Ur/izCwi4FliYbTA91SCU0wkVahRHSyTmy0RKJ8lHHarPVGDvFAv33kssoqDOlnl4bRdKQxCxG2zxyuiygTdbIjRWoXrMpep5kJEUj9Yo7DpGqFBloRFhJJxAPVFEqCmai0Vkvk61NcW4jKHFI7Q0xYkE5mhAwLJZKc15LivumJim+Q38LqPftixr2bngpmneD3wTGOF0StTPAr9umuZ7LMv6/tXO0bKsvYv38i517mpwWzvoisJ0RbK1WZA2Lv1QdD3JcREm4l1YfS8HJaYzbQsOz0BUk9yy9fwV/JEibM5JHA/qjl+T9GKlAJai7khemoawBlubuKLVMSkF8bDqr+THdZSIhlf1i1prLRF4YCNs6fFrkLafb0xWXpxh4etDAJSeHCf93kHqD5+gMVVBzRhk37TtknNIv6efwvdOIm2XxD1diHMiB6Lbm2n62Abs4SLhdenzzObXkmjsVQ77DXhVWZsWPDnmm6MhVdAf+GLXJSPHT9fxmxyrU696hCNBxHjAq84/XeZ5Erjgf05XqyUX+SzwR6ZpfsiyrLHFcTvxa9t/9lIXLxqaPRc43LqMeewEui3Lypum2Q/8wDTNMcuyloxitSzrc8DnAD70oQ9duFX6Mhgr+RGk4GcQHZo73yB9PeJVHIQQROMKFWuW0mwVJRQmlIlj39yBUMXpuuri9Ov0Tw8QubEJoQnCfWcbsV7dZfQ/PMv890cRtoYaV6ntSFO9YdBPmW8JYbdup/CvI+BC7LZWZN2l9tgpunRgdpZc8uLRlxO2xkxdo6EqNAwNpEHvgk1IA8X1UDVJT0KS2Jzlno+38ei3Zzi026NUdPHyDVpUh0gyiremlWgqQrZZ58jJBoc3dmI28uTTUYaqGqmhOXAUvOYk6bDEi+oYdZ10yaXFcWkOu7wzVEP51APMHn6chmoQ0UOkei8jMiMg4BplYbzGU7+3n4eLIeaTEXqkwm35IrJgM6XqZLwayUoNmwipsEO6N8XxiSyqE0bO1HA8gTNTZqA+iff5MSKRLpzeFmpH5nA7myk3tzE2PEs+1MFwNotmOyhIwtMOXkIjHZE83bONx4w02pDg9ukiH353mkjwfS0gYLmsiOY8l9UIKasAfws0TNP8KvAFy7IeW8b1f4JfN+pXLct6RRiapvnHwB9zuhPpq8JqrOQLITDbwHf0Lo2Uki8dlBxd7PZ+f5/C7Z1XJ2zLQuXr8Ta0uQKepjIbzfHhc87xgKh+Wnh6y5TpUko+v18ythjhNlYUPDhw9rxtV/LDYcl8HW5pE6zNnH282pD89UseC/MN1KjOR7aqtHx4HeXnplASOomXyw10XDhi0x4+vVIubY/Rx6coexKZCCEUGHlxgVzfxVPitUyY7PuWjk4F/zNNvOX10bk+4PqiNyX4xDYYK/nmaC7y+v/SG7DyxJMqpby/cBQ2FLRLNP8LCFgNpJQr5cpfrZYE+HdAH3DCNM2xxX2dQA1oMU3z0y+faFnWeSullmXddLHBTdM8CfTiR6qCb6Y+usQ4hTNeDy2av7ezMmn+l0VL1M9Ysl1fj3WtQtPNA7OSobxkICUuWlt+ORibs+jWNPaJAngSvSNGTlVwSzbVlMadW6LMTDSoVTwGNkRfWRAWQmCsXXq1sDFRwR4qIuoOEQQ1W0E2VHrXn25aFOqM0/TR0183io+dkcAm/THONV7PIqwR64uRPlGj4oBie7guGFIidEH3tgQ3PJChc02UsKHQ1BriEAJNFxhtBnpGRYuo2EKyMOMwMu1QLztkdYddN3QwsF2h+lyeOamSnStRjIVo/XAvqZcmKNYV5qY93tbssj6ep6mvichgho6PbeXUv5wiMRBn40cHru6DCQh4AzP1L6McO1pmKmPATJ3jtkuLF2JTqYCihWhyppESKq5NRmtw81szCOmy54kiIV2y9tgI/cVZytSQuoqdKuLqUU5GNaadJrLRCPlIhHxCxRUqXlSQzVdRXJcaBsVMkp2JLEa1weAzQzgTBUYf1ej69BYiW3Ov9dsTEPCGYQU151msRor9+03TjOEX0n8/8K+maY4DXwL+7uXozYvQB/zZmeboIn8OfOJy5nCpVX/LstwLHDuP1VjJXy7lBq+YowAvTsmrNkhnFlyqIR3W+g/imZnz35K39Qn+8SBUHLi3RxBb5hfuqsMr5ijA0YXzz/nBsOS5cf+c4wvw6Zs4K5X/6KxH7B8P0H5khnpTjJf+ty08eGOM9Lsvv8B8eG2Kys4ZwE/rqrTGwZsGV4IrKSjBil3AG5v2eJBWf72zxUwQNhQatqR/Q/SVhiUBAW9EVkBLwuVHFlwpXwE+CViLTZp2AO8796TFslETlmVJ0zSzwAPAb63y3M4iFRZ8bAvsm4XmiOCGlqt/PjRcybeOSUaLkAzBcMHXcs9PwIc3QX/66u+hhFSaPr4Rt2gz96WjOBMVFGDwgRZSb/cXxgc3nz6/um8Ot2gT2ZK7YDkqLWegNRkohkambONFNXK3xOjsv3BXd2N9mtKT40jbQzFUjMH0RefdPWCw/qYkrhCUix5dGY/8sTLzdZVUd5h3fbjtrNJOjYakUnEXG09BMqMTjigMH63ieZLiKZtYoUI22kCbLbD2365n6ESYQ6VmTqxrwwurtPdlcG/v4IWjLm6hwRPzVbZthXHF4OgXTuFaC2zoCBFu2DgnimjrLv4zBARcq8QMoOZ/75WA4kkiDQehKriKgbtlDbGUIJaK0LvBIZTWuGn/PmaLkpFTKoliA68Cx40UFKMYWojpyQbzbSn29zTTYmv039bK8Z1FKmWP5oUiG4fHiTouYQk1TWMyG2fNnnFiY3nijoN3uMjcPx6jY3P2vCzFgICAV5dVKUpoWVYZ+Dvg70zTbMYvZP9L+B0/L3VPC9iKX4j/TLYCuy7z/hdd9X+jEdF807BQ98Vny4U13CvUHck/HPQ4nocbWwTvWSvOSm/valKQusLJSQdNFdx60/lCsish+Pc7BFLKK0qNj2gQ0+GFSYkmYMsSGUkLZzSCcjxJ0RYkzygHmjg8S+ujx/HqLtETC2QfjcONywsijmzKonxUw5msEh5M4dqCx74xhZov4SSi3Lo1EIkBAQFvbPSQwqabEq/1NAICzkIIoQG34C9an1XLR0r5+Ytde5VaEsuyfucKp325/D7wv0zTPAq4wC9allUEME3zs8Apy7L+O35zqU+ZptnAn/ffWpb1zVWe23n4C2kr98X7yXInpgAAIABJREFU2XHYMy2RwK4pSVyHpoivGcfLgv4VklZCEWipME0f20B19wzCUIlsOz/KqvjjUxQf8QOFK89N0fypzYglOouqcZ2O/3wL818+RmOiTOKeLhJ3d1x0DnpblOZPbaFxqkyoK4aaunjdej2k8JZ35Ljz7VmEAlKCIqBe89DDynkLWEf2lKlX/apf89MOrZ0SKaFhS6QnCdds3JLNWFWSTOuoczXW3Zfj6e+VEa7ETep4MZ3REghdQcuFWYiH+a+zktkXKmwtenTbgqGSyoaUiz1SwggM0oDrlNa3d3HTP4+xMLfAqZYUGa/OOruCF9WgPULLoEo4AQgBG9rguSPExk5y3+F5po/VOCH6cEMGs0aMxrpOcpuz7NxXo9FhoM27FMZrDNdKuEmdUGeMll1z5BICW9FxXMnxzix1XadSdclLlW1OCRmGZadqBgQEnMXVaM4zWdWuLaZpGsA9+J1E1+HXFb0UfwH84eJq/DOL+27DL9D/GdM0XzE/LcvaubIzfn2iKoKPbIanTvmm411dlxa4PxiW/P0BSd2FnZOSgZRyVsRA0REIVZBTPTRVMCsuHEV5pV31JH6QZlQTqAo0lqj4uqNNcHzBN0f7UoK2czLdw/kqoYUalGxkWCM6W0Z6EnukhBrX0XLGZc0l3Jd8JR1qeGeFqXWtNNa2oitwYsxhbd8V/YgBAQEBAQEBSyCE2AB8G+jHrynk4uvOBlAHLkusXqGWXHUWDdz3XuDYfzrj9Z8Bf/ZqzevVwvYkC3XYOyOpNqA9Dk0RP5V/cBXKsCthlditFy79Wj/2SiUDnLk6znwd/QIRBaH2GK2/eun682eiZcJomYsbo+eiamfrZyN6vtau7plFjhaJCfCiOqGwwsDGKNID++gUx064uI5A1TVsVUHVFA5Nwo7tGtaOBGPzHrm0yu0DKmMl2D/rmyyjReiLQkMKdoXjtKtzSDyEKjAGg2LlAdcvWs7A/B+3YzwyS82GdFuIhW9o5I8XUTY1ccSMsSlTQeltgp5m2HuS2USU460K0fkZiuk4eSPDeCJOJJemURa0doYYbQC2JF4sgeKhJRT0ZIXGbe24J6eQ0sUWCvuNFI2G5FBPC63TRarzHu5Akuz71gbRowEBV8hKaU5YnSZNArgf+ADwnsXJfQW417Ksxy9jiC8u/v1fLnIMllls9Zw5vg9/5T8DvNs0zc8AD1iWtf9Kxns1yEUE71xz+Q/NA3OSugM4LkWpcnQebmg5fbxSdKHmEl/sbF4ZrwOXEZp6GXhSoghBzYGaAz2LZZpmq+fPfzAj+Lc3QdEWtMd8M/hMpkIGRqGGVm3g1RtMuwpzXzzsC2EB6Z8cILpEJMFZSAnPHIbJBdjSQyPWhKIKwkKCAPsCaVgBAQEBAQEBV8wfAS8ANwITi3+ngL/kEinmK6AlMU2ziK8Vl8SyrEu3UQ+4ILe0CT73ot/IszkK/Sl4aMCvJZ85o/lo2ZqiMV4hsjl7WZ3rr5RwX+KVmvNqOoSaXp6ZuVqMHK8yMVInmdEY3BJDOUPn2qfKzH/tOJuEIF8N4YVVNt2cZsfdaTzrGPPT03SWHUpFj7FsB1oshNNl4ArB0d0l7gKMDQYx4XDwx0ViCZX3DcYpeQrpsMdCXZDO6RTmIHdHC1s6BNl1CfS2ldH7AQFvVMJRjZvf0Uq95rHn+SLj69pwBlpJZTRGqtB8cxutnYvPkG19DH93CDumUzF7mM32ERc667piVIWKlPDe97fx3J46L/1wlshYA9eBZKOBVwshmrM8M9BBeqFK66YkcmOWI7UQxVyYPffdxIwo8O/fnSSyIfi9DAi4Cq5Yc57LakSQjgNJ4LvAR4HvLLMD6eUXl7xCLMv6En4dq2uWm6M1fjy2QMERNIUlW1MtwOk6tq1plTVZhaF5j7AKZu/SXvPQ8QK1os3azVl07dJ1cP/5mMcLk5A1JB/Y6BfqP7i4mr29dWmDNxUWXChbKZ0vM5MK4ykCN6yRKtaoH1uslyqhYk1d2iB99jDye7twpUDbM8zWjz7AP2QjzEzapHMaN/QGBmlAQEBAQMAKswO4W0pZFkJ4gCal3CmE+A3gT4GLhfBdrZYE+JVztnVgO37K++8uc6yAc4iHBPf2wHABNEWgKn5t05B6tjma/+dhAKq7Z2j65KYLRnVe9Xze0oGaM/AKNpFtOZTQa19ffm66wd7nfdN2ZsImbCj0rTv987sLdZDQEfV4a7yE16+x9oEMiiJwRuZAKCSSGp7boDnmUk/rhKMKU6N17JqLEVGxR+pIIBSCoYMV1N0l1m+N854tcb45JIh0hXjgjhDbW4MSLAEB53LopRInj1ZYmG1QKblEYgqhkHLWQgbdTZx6+11MffUIQ9E00XSMd70tx4s7y+Qn6hhhhVrFY22rSrEvTMmJEZmd5Y5wnrkNm/mLeYOJN6/HrntsyUgGe8OkRhRkwSWkSIbTcTr6Li8jMiAg4IJcjeY8i9UwSP8j8BXLspZoyXNpLMsaXuH5XJPUTxRY+MYQuJLUQ70YG87OZ7rr1FEq1XlGRYRNdpF1kza0nfaeQ2GFn3lnmuEjVYyowuDm87u4v/DdYxT+6XmElIxv6OItv347inJhk3SkILEmfDN0tgqPjcLPrhecyAvCKnReQdfUbJfBeEjh2Jommsp13pFTEGEVWfdN0stJsa+M5nl+LkfF0WgN1wjty1OpJHBUl1pNMDZcozMdrNoFBAQEBASsIAK/Gz34nd47gUPAKLD2EtdelZYEsCzrb5fav9jI8158wRxwFbxzrcLXj0iqDtzfe7Y5Cn6395eRrsSZrq2aQSqEuPSC+atMrXJ2A9Rq+exaU+GBJFpzhIk9eebzLrXNMWYfmeeun8gS2tzBumd3cZgYrtRJ78jhGiFqZZd6zWPsRJ2etQauC54radiShbkG8aTG9HidVFbjV28+X9sHBASA0/BQFMHJYzVGh2pIDxxHUhEK6Z4ITW1+8Ex9uEjtwDy9exf4VmcPDSmIS/jSpMZUKYQW0bi7zWP/CyVu7pQYu8eRDUEyHifz85vIKxFKT7rEEwpFzWO/oRM7vkDPrILRlyUaUnhTP4SNVWnGHRBwPXE1mvMsVqOL/V+t9JgB55P/1gncBT+YYv5rx2n7P246q1aoGjd4yJ04fUF8y3ljZJt1ss0Xjp4sP3EYIX3DUzk4SnG2Sqr5wmJLPefZrgpQhGDgKurAD704w+ODHTSkYLRJ0jZR452/tYXS0xOoCZ3EPV2XHiPaTrlRxnMkE0aCQiHEmh89TdvUNDPZNEd738wtNwQGaUBAQEBAwAqyF7gBOA48B/ymEMIFPgEcvdiFq6wlH8VPxQq4Stpigk/deOHF78jmLNVdM0hXoiZDhPquryjG5vYQ8aRGqeCghxQ6+/1Ffc+VKKpAMTSaPrGR41+boFoXeGGVqRM1nv7hAms2NbHm02+ib2IBsbadqZLK9748zfhIHSFADwnqVY/OPoOwoXD8YAVVFWQWdb0XNHwJCFiSA7tKnDhcQQsp1KseqiqoupLJTJRCX45dBY+R52x+ptdl/vOHkK5kcqRBqjVOJRziWEWl8XSBjOugRzSOOjqdB6YoPz3Puv1TOCGN9I5m6rtm2PiBQbaN1hlagFlDsOHQKOHDs+xwYSZfJPcz/fziDa99tHtAwDXAFWvOc1nVJk0Bq4c8U/gs0fzIMdfwrZMqYzM2G/ui3Lembdn3iLYmKI37wRtqLEQsGbro+R1xwd3dAmsCchF4a8/VF5oesx1sRSAkuEIwIiHUHSfbvYyFgEiUskgjGzaqGqPj4DF6n92Jbrs4uoJcH4N33n/Vcw0ICAgICAh4hd8FXl5V/S3gO/jm5Ax+R/rXip9fnEPAKhPuT9L0S5txpquEehOoseurpJEeUnjz/RlKBYdITCUUVti/s8jw0RpGRGHHXSlqNZd9Ix6zUzaJlIYQUFhwePGZIvEHMiTNJgBO7l6gXvVe6XZvRHV23J2he42BqgrWb4vzyLdmmZm06ewz6BuMMjtl07AlLe0hFDVo/hIQUCm5nDjsB5k5tke9YCPjGierKru0GLnjNtGqw66qZOiRPHdMe6zLwEC7TlgRHLY16oqgZaGC0ARSFcQLLr0JD1FREIpAtx1k1aEW1jm6r8LPDqiU8nkOfv4FjKfzHMm2QSLGjlqBji6FpmjwuxkQsAKsmOYMDNI3KKmHeln42hDS9Ug91Htep/lnJlVe6uqHLngC6J6TrM8u7wF848dNfvgPcYolh7f8RB9a+NLC9s0dgo44ZMKQCF3e/aTrIW0PJXL+P8cus4XI7uNUbBVN9ejesfz0qZZ8gfGwpKyGaKFO5+gswyGVyWScdL3OmtkrzuALCAgICAgIWAIp5cNnvD4ObBRCZIF5KeWqh7eZprmHs5s0CaAVyAKfWu37B/jozRH05shrPY3XBCklritJZjSEEBTmHYaPVAEozDf40XdmGR2qUa+5qCoszDTYeFMcRUCj7rJ/Z5GWjjC9gxEiURU9rJBIq3ge9KwxSGY0VFVQLro89YN5KiWH1k4dIWDoUIWhQ74RlGsNccNtCTRNQdUCMybg+kVRQSgC6Uka01WyQ3N8P5JjSteZaQ5TnLDpikjshmSkrvItLc17qkUG45IP3Rnnb/YK5GQFo1RmIRriZr3Kz96eQ5VN5L9bIbIlS2O+jryljf1KHOeA/zt484Hn6dAK7BINYgtFQukI+2NJRsb9BY/epGAwE/xuBgRcKSupOQOD9PWA58G3nofjkzDQCu/aARep9QlgDKZp+83tSCnPM0cB6q48Z3v50/raSIjPJbbgxOC5McH/s9bvTn8hbFfy13sk0xX/vJ/bwCVN2cZ4mdkvHMarOES3N5F+99k9ujbvaOPGb4yxd1rQEva49/7uZf8cRibMpvTpN+Dk9i38idfJSCxJW6XIr9yTY/mjBgQEBAQEBFwIIcTfAL8qpSy+vE9KOSeEiAkh/lRK+QurPIV/Omfbw69L9SPLsg6u8r3fEDRsj307S1SKLr2DETpfhUYhB2clPzwpMTR49xpxzUZP2XWP5360QHHBIZXVueUtqVek/fy0zdDhKpGYSqXkoumCaFxF0yS6ruB5kqlxm7npBgdfLLN1JkH/hggvPafgedDeHaarP0I6p+E0PJ59ZJ6Tx6pUSi5OQyfXqnDyePWVuRzcXWJyrI4RUbnp9iS51otnhAUEXKsYEZWtOxIc21+hcbRCf9JhNhphXtHIhEFqknDVoTZtc1TVKG/qQY3b/O7dGk1Zg7vn5nm2rFHJRskkYcvWKAmzGaEI1GyY0bE6XyNNqSZpHs7T7LnMTNokDttsmZxmh+HRZVf5blMv9tt6efoUPDXmsaNNcE+Pwl3d1+bzMCBgtVlJzfm6M0hN01QALMvyFrfbgHcAByzLevK1nNuqsWsIdg/5r3cPQU8T3LTmsi5dyhwFuKVdsH8WZquS3qRgY3b50/rWMYmzmL7/3IRkvCQv2mhprAjTFd+Y9aTkpelLG6TFx07hVRwAKrtmiN3Sgt5+us5pbd88d78wzm1FGz2s4vwoBR9dXlHT+O1teJUGjYkqkW05HnczPDfehla2mejs4AftBvcta8SAgICAgICAS/AR4DNA8Zz9EeDDwKoapJZl/c5qjn8tcOilMuPDNQBees4hndOIJVbvq4HtSv7psMRZLBP17WPwsa3XpiEwOlSjuODr2/xcg7ETdXoHI3SviXBkbwUpQVEEkZhKveaL7S07ktzxQJb5WZtvfmGSSslf3N/zfJFq2SXbrJPK+tGoN9+ZRFEEpbzftCmV0amWPWpVj0yTjhFRGB+p02hIykWXpjYdp+FxZG85MEgDrms6+ww6+wzyokj52RJ3uQWeVJLoKcHmzhDlxws8HQ5zKm5guArRrhhek0JEF3S1aoyM1RlqjaBsTPKIIeg6WaWrL0oxHuVrRx3m6kX03hgjdQV7poqOZHrLJiaHp3l0/VqOtnczq0TIqAolG0KLJUgPzsnAIA0IuHJWTHOumAoyTTMK/D7wHkAHfgB82rKs5dZ5+g7wPeCPTdOMAxZ+PYG4aZoftyzr8ys159cNDefsbdtZ+rxlkAgJfmU7VB1BRLuwkXoxcgaMLv4Ti2iCS5QgJWOArgoai9GrLZcRFSDCZxSmFiBCZxeqrh3NUzuwgFd18DSFyq4Z+OhyfgoQmkLqwd5Xto1Hq8TmbaQnEbZNuCB4Ha4VBAQEBAQEvOFYTGkSi38yQogzRY0KPARMLnXtCmpJTNO8G8CyrMeW2C8ty/rxcse81rDrZxSxlxK7Lokt0UfJkxLbBeMi6dmOJ5muQDoMEX3p81yPV8xRgNoVZDe9UdDOea+0xfeko9dgzaYIo0OCasVDUWDzTXG2vzlFz1q/FEFLRxhNU6gvNhkIhQQN23+tqgKEQCCYHKszfKRKqeASTaj0rDXoHYywbUcSz4NYskKl7Pq/jIvfA/Rw0C07IAAgeV8XQlf54HydbX1RRLNKe6nBlzMa3a7Aw8NQPTYmIaKr1CouU2N1vJCKZkP4+Un6xqeZzgmSD3bwYt6AuousQuNEGWNLmpbDDaIhgRGN8uztd3IknQRAbYozUwNDg76k/7vZdX31sAsIWBGuRnNeiJV0hX4H37r6IlAF3g/8JfDeZY5jAr+x+PqngALQD3wA+HXg2jNItw/A3pMwOgtdOX97BRBCEL2Kevj/+1aPP/1GiWrN4113xkiEL556lTYEH9wIu6chZwje3HnpeyTv78YrO7jzdWJvakXLnX0Pr+YiFw1X6Um86tWbxz0HpsmUdQohnVjdZc3+eXjn5UXsBgQEBAQEBFyUGfzanxLYv8RxCfxfF7h2pbQkwB8Cn11ifxL4beDmKxjzmqFkS+ZyUcZHXdpxaOkMk86d/7VgvCT5u/2SckNyY4vgPYPnG2x1R/I/90omypKIJvjoFmiNnW+SRnTBW7oFPxqRhFTBfb3XbrRU14BBYcFhdtKmqT1ER28YgEyTRnuPgZQwcqxGKqcRCisc3Vema8BAUQSKIrjt3jT7rKLfgOmGONlmnT3PFfEkrNsao1rx2PVUAelJIjGFVEZj/bY4ze1+NIOiwuAWPyOrpSPEsf0VwobCpu3x1+w9CQh4PSF0leR9XSSB9sV9xXyILS0K6rRDh+fwgenn2f7VaXgyjfa+u9F0hUzY5YczKo1aCOJxbjfKlJ6YwN3Uy2Ba4HoSR5W8Z6tGujfDwRfL6CHB2o+t4dB+B6EL4jkDKSWtUXA8uLtLBNGjAQFXxtVoziVZSYP0p4CPW5b1DwCmaX4ReNI0TdWyrOWsEceBl7vmPAB83bKshmmajwB/voLzff0Q1uHf3A92A0Ir1+FztiqZLEN38vIbJp3JzMEK96UakALleBFnSwhNv/jKc29K0Ju6/HupcZ3cB9dd8LjRnyC8JolXcVDCKpHNV1Ar4BziNZu7huep6CoRxyMVD6JHAwICAgICVoi34q/kPwL8NDB3xjEbGJZSnrrAtSulJQHWAy8usX/v4rHrlroj+QPL49CcQIsneWsbrOlXlsw2+vGob44C7J6S3NIuydfh8LykJyG4sQW+dkTy2IhHcxSyBrwwCT8xsLQWfEuPwps6JKoCmnLtGgKKIthinh8SJoTgpttTbL4pwZM/mKNe8SND6zUPz5Uoi+/J5psSdPT4Jkq22Tc9WzpCeB6Ewgqzk34mFPhRpbGE9oo5ei7t3Qbt3atfXzYg4I1I3ZHM1vzMyURK4x3vynD7dIPsxDjR70+DAGd0nqEv7GZ/bj3/OqcxHNMxQjoPu1HeUR+mI6mw+aY4+14ocXOHYNutSdqaBTT7ZTUA6jWXU1XBCVtDUWG+BjFd+M3cEKjX8PMwIGAVuRrNuSQr6Qx1A4+/vGFZ1nOmaTpABzCyjHFOArebpvlt4G2cjhrIApUVmuvrkxU0R0eLkv+116/zFNMFn7wBkuGzH7wNV3I8D8kQtMfPfyifmXrlOhLHkWjLnOJMRfLUKUlYhbu7xUXTs5YifkcHuQ+tp/TEOOG+BE2f2LS8CbzM+BxMF6C/lb71BqlH5gjXBCEV1m5MXtmYAQEBAQEBAWchpXwMQAjRD5xcZvfQldKS4EegtgND5+zvxBfN1y0jBcnjo36d+ckKHM/DcE3ypg6Pt/WfvRB+ViUkIZgsS751zO/QvmtScnBO8PyEZLwMExUwWyWpM9K4y7avA4WA2zsEEV0QDjqpE44oDKyPcmB3GaSkqz9yXhBCpuls0X3m8XSTTjqnszDbQNUEPWsCAzQg4HKpO5LREmhC8tUjUKhL0obg41shkVisxWyfXnCYHrc5rMBzYZ0jukYtpKMmdUJlm9m2DFseakFvidCx2OhOOcfsLMw7PPujBWK2x60pjdz2FF8+KnAmazROlDl2RHDjgyma2oL6wAEBy+EqNeeSrKRBqnK+4HSu4B7/DfgCUAKGgZdrRN0F7LmaCV5P7J89XQS/3JAcWxBsbz193PUkf7tPMlqUCCF41xrY3nr2w3zNpii7ny7guZKewQhG5Oz6oJfCk5LP75cU6v48FurwcxuWL4qbfmkz8Y9sJGwofu2l5XLkFHzpCfA8SESgbZBt+4eg7oEuUIqBqAwICAgICLhahBA3AbullB6QA3IXqoEupdy5xO6V0pIADwP/1TTNd1mWNQ9gmmYW+L3FY9ctigKKAE9CtQFKxDc8v34UJsoeG3KCW9v9z+2+XkGp4Uc73doucDzwP16fYwuSZMhvyjlVkWzICt7UcfpeXzwgOVXydeBYET6yJTBHX6ZvXZSmthCuI0lllxeBoKqCW96aprjgYEQUjOjyNHpAwPVKzZH89UuSmapktAjxkF87eaEm2TcjuO3l59f6Trh1HRwYpdiRYWRggMmqgiIltgeeqrB+XYTb7+1FX6wxfK4x+jIjQ1WcxTrCpbzDmnqDm1t0nn2uTCYk6TBg/84Sd/3E1WdKBgRcL6yA5lySlTRIBfB3pmnWz9hnAH9lmuYrkZ+WZb3rYoNYlvX/maZpAT3Av77czR44BvzHFZzvNU1bTOCXXPBX/FtjZx+fqfpRpuCL4henzzdIWzvDvPWdOVxHEvn/2bvvMLuu6uD/331uL9O7yox67z6We8d2bIgdOgSLYgcHAiH8klDekDckJIGUH+F9CSGUAKE4BAw4OKFj416PZcuW1euMymh6u3P72e8f+47mTp+ruZJGmvV5Hj265Zx9zp1RWbP23mtFCg+8EhlOJ0cB2s5g/W82q3nuU0/Rta+bUGWQrZ+8kmhtuLBBdh8zyVGA/jjeVw9SkYijXVAueJ49AfeuL/zmhBBCCJHPAeqBttxjjYkPR9OYZOhoRYklc/4UM8l+xLbtl3Ovbcjd21uncf5Fq7FU8VuLFS+1abIa1lTBiRh0JzSHe+Fwr6Y6ZLG0XBH1K+5aM/wt7Elowj7FYNrUEb2xEX51FBoimtVVFm9frbByP6BorWmNDV+3pV+zv1tTHoCaaTTynAuipWf+o5jHoyivKt7uMyHmgiO90BE3P5/6LM3RPmhRoDXc1JT3X5ZScNsWuG0LZceT9PwqxvE4JHwe5kXhg5vgLausCZvS5QuHRyzFJxzx8NoFFv6FCjc7fDkhREFmGnOOq5gJ0m+O89p3ChnAtm0f8ATwTsdxHsh/z3Gcn8zg3uacDTWKjGtxfECzskIxb9QW+lK/6Uwfz5j/IOomyDn6AxYEzuwewj7FqirFnk5zjc21hf/Lf/RXRzj06gAaH92nsuz8xk4u/9jWwgaZVwnbD5nHXg+lW+fR89MTpNMar1dRsb664PsSQgghxBiLgfa8x4WacSw5xHGck7Ztb8Q0+dyUN/5/OI5zcZdsmoLXUlw7X3GkF7bUaZZXKFJZ6EkMH9OXHP/c8qDi/ZvgWL+iPgIVQcWyck1HXNFYamK/IUop1lZrXmk3O5faBhX37XKxlOKtq8yqUyGEOJcqgyaD0pvURP0QTEIia153WuGaBWPPqZsfoGOBhxqPxvUofBasqbaITLPHR9OKEKmUS19PhoaFwdMTGxsuK2H3izE8Hlh3qbSxF6JAM405x1W0BKnjOO8pwhhp27YXM7T08SKVdTWPHdO0D8KmWsWKsxQgbqlTbKkbf+yQT/HOtfB8q0mWXr3g7NzDm5bDditDOGCxbkHhq1BTSRetQbku2lKkM4XfZ2r9EvZuj5Fp6Sa6dRFL6zVlFRn62iFSAuWbClyRKoQQQogxtNZHAZRSPuADwL8MvTYdxYglR403CHy1mGNeLB45ZhICoOhLKt6/Eb7xquluXx1SrJpkp2eJX7G6avh5dVhRPUEo9YblinXViuP9mkePmfDe1ZodbZIgFUKce7URRWVI8WqnJqPNZFBdGOZFoD9lVr7nb9NNZjTPtcKgBp9fkdUQ9MLS8uExW2OaPZ2ahqhiYcBl1wsDpNMuy9dFqK7zY1mKlRuiY+5FGqgJceZmGnNO5Jy177Zt+3rg9xzHuWuKQ78JvBf4yFm/qfPk8WOaR1tMkLi3G/5gE1SFzn2Q2BBV3LHs7F73paf66TyRpBMIxSIsXROZ8px8S25cwJH7XiHeFccT8rH69o0F38PB3YMcK2uAsgboB+twC48sXUnf8gARnebOFzupu77gYYUQQggxDq11Win1B8AXizluAbEktm3/LdDiOM6XRr3+PmC+4zhzumxTwGOK/Q89rgpbfHCzpiepqAqCb4Ka71qb5k7573cnTNMnrwXXLVCnV1UNJRoqApp9XZquXKdogBqZmxZCnAeDaU1nXLO4DJ5rNSXhjg+Yusx3r+d0cjSe1jxxXPPzwxq/pfFailK/Oe+2xYrqsGma1hXXfO0VTTqrAc3KvhjluSX425/o5aY7q9EWvNoBPgtWV8FEdRKFEIUrdsx5VhOktm3XA+8B7sYse31iGqdFgHfYtn0z8AIQy3/TcZwPFfs+z7VwWXBeAAAgAElEQVTOvC1MWTcXjIbO3/2cLamkS/uJ4T1ax48kC06Q+lvauGlVkt6lfsLeLOHWk5imtNOn3ZELkvdly+n2p0hnFVmPl5d1FTcXNKIQQgghpvAL4Ebg6zMZ5AxjSYBtwJvHef0F4H8xx+vav3GF4ieHIOuaH/YBgl5F/SQ/GZwY0Ny3SxNLay6fp1hdqXj0mOY3LZr6sCbgUbQPwrY1sOOZPlqPpfCWenm2pJQM4EET9llc3qC4av65+ZxCCJEv4IGIT9GT1LguhLywsAQurYfrFw4nLn+4X3OgW/Nimyboha31pnbyx7Za+PMmiE4MkEuOGi0DMLS4NJvRZLOa/9wLh3rMMVsbFLcvkQSpEEVWlJgTzkKC1LZtC3gd8HvAbYAF/BXwFcdxWqcxxGpgqMvUklHvXRRb7zfXKnZ3QsbV1EUUCy+ikiPPndQ836qpDsHrFkMw7CExaKpPl5SfQYfNcACfpakOpE8/L9SilSH2vhyjpzPNktVhfBVldDydJJnV+C0FjeVTDyKEEEKIQjwEfFoptYFxJry11j+a6MQixJIAtQzXpsrXCdRNc4yL1ryo4r0bJv8hvTepae6DhojZRv9ws0mOAjx53KyusoDDPZreBGyoMU1A246naG0xE+TNHVlaB9NU1/koDypqQ5oNNQrPBN2ehRDibPJYim1r4ZFmxZ4uzUDK1B49NQgumnethbKAOt1grioE7YOmNMjaypHJUYCFpWZyKZExK+avWBNgcFcS19UsWhEGr2L/8RRuLI2nLMCeLg+3j85wCCFm6oxjztGKliC1bXsZJpB9JxAHvg38CbAL+MF0A1rHcW4o1j3NVkvKFR/cDN0JxYKSibcxTaWlz2xzWlQ2O5bqn4ppfnrIdItvHzQ1qq67vozDe+N4fYpla85gP9WiWgav3kDn9lZKFpZQfuXKgoc4cTRJZ1uKwf4sxw8nqF4Y4NV55XTioYws153h118IIYQQE/pC7vfxdv6M21G0WLFkTjNwDXBo1OvXAscKGGdO6klovrxDE8+YraXvXme2hwLE0pqDPabr86pKqIvAQMq8t6UOlDU8TqXHJZprtH6kV3NqUHG0X3PLIrhinsRfQohzrz6ieNtqDwd6shzo1hzogWTWbJd/5gTculixoQaeOg5rqhSlDYpbFinWjtPXtyygeO8GONCjqA9DU1mA1LIqshlNKOIheaSPiNNJO16Uz2LtzTWA/5x/ZiEucgXHnBMp5grSXcC3gLc4jnN6+5Nt20W8xMWjPKgon0FN5oeOujyeK3a/qVbxO8vPf5CZyIx8Hs9ApMTLgnVRfBZ4fYXfY2Iwy9MDtaSaqkEpLunIUttQ2B/bHc/0c+yQqWvQ05mhxeulS3lQQB8edse83F7wnQkhhBBiIlpra+qjxihmLPll4HO2bfuBh3Ov3QR8Bvj7MxlwLjnYA/GMiTMzrmZPF9y6SDGYhgcPQk0I+tOaXV2mKehlDaYZ04IShdZ+Fi4N0dqSpLrSyzVbfBzqV/znXtMYVGvNYy2SIBVCnF9LyhVtg6Z+cnluk6I/l0a5ZZHFsnKzGGlZBViTLEaqCo0sl+cPWJAbL7G7mzelunjRE8af0VxPEKgadxwhxJk5w5hzXMVMkO7GbIPqtm27z3Gcl89kENu2H5zsfcdx7jiTcS82208NP97RDncs05P+w30uNJbC6irF7k5N1K+4er7iF4ddnj6h8ViK31kG62sKu8fO9hTHDicY6MsQCFrMa/RT21DYNnvTJMBsjVCWQmXBzYKrQSnwkC1oPCGEEEKcFUWJJQEcx/msbdvVwOc5/aMqSeD/Oo7zDzO/1YtbfcTsTtLaJEkbIoryoOKda6G5n9zrCgX8sW1R4h+O75RSrLNLWGcP15CqLoGnTrr0JMx4pYVXTBJCiKJ68wrF0nIPK05q0q5mQYniqvnD/5YtKZ/5z9a+eRHCtHFV1nSCCjdchI1HhLiIFC1B6jjORtu2LwXuAR6zbbsZszUKCqsd2jnquQ/YCCwEpl074GJXE4ZYr3lcFZx8VutcUUrx1lWKWMoUs0678PQJ863PuprHjhWeIE2nNH09GbTWDMayDPQVnsxcvjbM7u0DZFKaYKmFv7Wful4vCa+FP+uS2JGFu8bZMyGEEEKIM6aUqsAkPBsZtadQa/2p0ccXMZYc8nfAT4Gh9pi7HccZmOR4kTO/RPG7q2FfFywsVaytNvGbx1JcWq957qQ57qamkcnRybx9leJXR0236Fuazn/cKoSY23wexWUNcFnD+P8exVKaXZ1QFoAVlWf2b1Z4YzVoSJ+IEVxVga/uDErOCSGmVGjMOZGiNmlyHOd54Hnbtv8YeAsmwPUAX7Nt+z7gR47jnJxijPeM97pt258F+op5vxeyN69QPHoMMi5cu+DsBJmZtMv+VwdJJV0WrwhTWjG9Py6RXKCs0QS8imRui9ZQDapChMIeSis8tJ1IUVLmpbK28EG8fotl68MkYi6llV62P95DU1/q9PtdqUlOFkIIIUTBlFKXAz/BrNqsAY4DDbnnR4Bxg9VixJK2bTcC/4IJlIeCJA381LbtDzqO0zyzTwe2bd8FfBRYA3zYcZwvTHLse4GP5e7lZ8CHHMdxZ3oPZ9vyCsXyirEx5u1LLDbXajwKaiPTj0HrIoq71khiVAgx+6Wymq/t1HTFzc+xv7XY4vIzLAsS3lQNm2QxjhBny5nGnOMp2l79fI7jDDqO8++O41yD6Ur/BPC/gZYZDPtl4APFuL+LQcSvuH2JxR3LLMqDZyfY3PnCAEf2DnLiSILnH+shmyls8YbXUrx9laKxVLGiUnHnssLvUylNa0uKWH+WjtY0gwOFryD1eBTlFT7qFwQIhz0EQiPvw3+Wvn5CCCHEHPaPwH3AfMwKzhsxs/oO06gBeqaxpG3b84FngM3AXwBvzP36JHAJ8LRt2/PO8DPlewl4G/AfU9zP4ty1rwCW537dVYTrn1cNUVVQclQIIS4knXFOJ0cB9nWfySYGIcQ5MqOYM19RV5COx3GcvcBHbdv+BPC6GQxVePvyOWRwIMtLT/cRj2VZsirM4lUzX74/0DvcdSmVcEklXULeaTcAA2BRmeLu9WceQJ9oTtHm9dLrtQgFLFpaUlxS4BhLV4cZ6MvS35NhXlOAVDLDww/2nH5/xbrIGd+fEEIIIca1AbhHa62VUlkgoLU+pJT6GCapeN90ByowlvwkcBh4jeM48bzX/8u27c8Bv8wd8/sFfJbx7mkngG3bU60EfRPwX47jtOeO/yrwHkwzqlkrm9G8/Fw/vd1p6hcEWLUxer5vSQghzpmKIJT4Ff0pkxhtLJniBKAtprl/n2YgBdc3qgm37gshiq5oMedZT5AOcRwnDTww1XG2bX9+1EsKszz2NuDrZ+HWLgp7Xx6gtysNwJ4dA9TODxApKSyZOdrCpSF2vdAPQE1DgGC48AXHqRMxBh49gQp4KL1lIZ4C99n3+D20H08S83iIZ9McjI1N/GazmhNHTXmx+U1BLM/I/4x8fgv7mrLTz3c6/eZPlekvgOvKf15CCCFEkeUXsDkFNGGaMA0AZ7SCc5qx5O3AO0YlR4fOH7Rt+8+B75zJ9c9QI3A073kzpq7+rBNPawJeU9f+8L5BWltMbHV4zyCVtb4pm2Q+cUyzo11TF4Y7lin8HomvhBAXpqBXcfd62NEGZQHFptqpz/n5EU37oEmo/vwwrKli2jWahRAzUrSYs2gJ0twM+pRrzx3HmSprt37UcxdoB/4/JEE6IXfU+gXtznwbQNOyEBVVPtIpl4oaH6rARlA669L1nX24g2YlqjuYoequFQWN8eTDPeytLMW1zLXVSwOYshLDnvpVN3teMj0X1lwS5aqbK8eM09eTYaAvQ1Wtn87OzIg/qF3dmTHHCyGEEGJGtgOXAvuAR4C/UUrVYbaXj9udvkixZA1wcJL3DzA6kBj/XrZjkpvjqXMcp/CaP9Ng2/a9wL0A9fX1Z+MSY7ha8/29mj2dmtKA4l1rIZMe+W0Y/Xy05j7Nr4+aYLR90Ky+uimvEVNHa4pjhxNESjwsXR0eM5kthBCzTUVQcX3j9P+t0jr/sUZr+XdOiHOk4JhzIsVcQfrmvMcKs3XoI0BrIYM4jnNDEe9pzlixPkJ/T4ZE3GXxyhDRsuJ8a6fbmGnIoy0apzlDVanFGxrd08lRgGxPsuDrnziZQlvh0x0W+pJjV7G+6vSTSpmgfKczMCZB2n4yxQtP9KJdTTDsIR6w0Dq3iFRDPHDOFlILIYQQFxyllAVcA2it9WNKKaW1niqR+QlgaFPin2Piwn/GBK/jNuSkOLFkG7AMODbB+8tzx0zKcZwtBVxzMs2YlQxDGpmkjqrjOF8BvgKwbdu2ohW962hN0dmWoqrOT3XdcHPXrGuSoz8+4NKQqzj0xHG4dXmItuMpYv0Zqur81M2fZPWo65I40gmxCETMcfG8uefBgSwvPN6Lmzd5v1zKGwkhLjK3LFJ8by9mi/1CRWlAEqRCFOocxpzjKlpmyHGcH+Y/t237G8DPHMc5dCbj2bYdxAS4GjjoOE5i5nd58Sop83L966rQWhe80rNYTvS77P72IRoOdJKJ+Hn8nSu4anM1gy92gILIFYWvhKj2ungyGjf3mUrS6THH+IPW6QRpIDA2gdp6LHl6RW1iMItKatJehdIarRQBr/znJYQQQkwiiukA6sc0G7KASVdQaq2dvMftmFJJkypSLPkz4G9s277JcZwRM7O52PKvgZ8WMN5M/RB4zLbtvwI6gfcyRWOnYutsS/H8Y72gNYf2xLnshjIqa0yS9JEWzfN7Bmhtg/aIn62NXoIeRTDs4ZrbKsikNT7/JCWWtIb/eJylB06yzNvIgVUrKF1UyRV53Z4HY9kRydFY/1lZfCuEEOdVQ1Tx4Uvk50ohZuicxJwTmXVL52zb9gGfBj6I+aIoIGnb9j8Dn8jVn7ro/abZZfspqA7Bm1YoItOsX1LM5OjgQJadTj+ppMvydZHJVw8AqeYBSg90AuCNpfA8e5Ky9y3BU+pDRX1ELplyR9sY3sEMqzv76A75iaQyVCdSY4657vYqnnukBxRcfmP5mPfzV8FaHsXaFQEO7oyRzoLfo1m3xD/mHCGEEGIuUyag8GitM0A/8Cjwjtzbs7md719iupYesG37C8Ce3OtrgD/AxL5vnelFbNt+O6ZragVwp23bHwducRxnl23bnwJOOI7zJcdxDtm2/dfAM7lTf8m5rYFKT0d6eO+n1vR2Zk4nSNv2d1K6+yRLVIiTvWHmL6rguoUhwMSUvqniz54YHDiJB3hHpplYV4LQG27AYw2fV17po6TcS39PBstSzF8UPBsfUwghZrXepOZ7ezRdCbisAW5oLLy/hxAXo9kUc866BCnw98DbgfcBT+Reuwb4DCZ7/Kfn6b7OmWP9mkdbzJ+D/hQ80gKvXXruZ6NefaGfzlMmIfnS033ceEfVpKsI5ld5aYgqWmMQ9MDaBg8vfPMQj5y0CJDgjq4MTbfOH3Ne56kU8ViW2vkB/KNWgM5rDNB+cpBoyuzVCoyzI2vpmjALlphgOxAce39Ny0JYytQhrV8YwONRnDyapLM9TVmFh0uuHptUFUIIIS4GSqkQcCuwV2u9O3+rUm4bk6k4o/WIaua5Y4Y2SpcAr5LbKj762FHXe3A696W1vqPQzzIdjuOcsG37SuCLmAn3oQBKA78APug4zvEiXOe7wHcneO8vRj3/MvDlmV7zTFXX+zmwaxA3q7E8iqq8LfYb+1rZrzWNxNma6uaekgw+b9Mko40SCUDID/EUCojWhMEaGbN6fYoN15Rz/84MCSwW+DxTF4EVQoiLzMPNmhMD5mf8R1tgdZWmPiIrTsXF42KIOWdjgvR3gbsdx8nf/nTQtu124N8oQoLUtu1/AW4CkpjOVn/kOI4z+VnnTmbUH4HMWcqZp5IuJ1uSBEPWuKtD06nhC7tZTTarmawHva8+TNPKKNH/Poq/IULFpdV88dvdpHKB8o93Z/nQrSPPaTkYN13lgfDuQa66pRKvL+8/ilFF/JVv/ATteInRfAuXhkY8v+2ttbS3pqis8VHTICtIhRBCXLTKgY9i6jPtzq/jNEXQuRD4P8DNwEvAk4CllFqstT48yfVeh+na/sjMb/3MOI5zBLjdtu0KTM1RgAOO43Sdr3s6n8oqfVzxmgp6OtJU1PgoyatTv2ZplD+67wFS/UnK50XxLXx3YYP7fbDtenh6L0SDcP26cQ97+Di0pE1vrR/u0ywsQerzCSHmlNE9lIvQU1mI2eaCjzmL2cX+86Ne8gOftG27N/9Fx3E+NMVQZYzfffQg5gteDD8DPuw4Ttq27dcB3wOWFmnsGWsqhY21ih1tmsqQ4pr5xQ8gsxnNMw/1EOs3ifqVG6MsWRUecczydRFefKqXbEazeFWYYGiyprHQd3SAfY900BOMEOjRuP9zkkyoBBLmGsmysVuqTh0f3jI/OJBloC9DedVwGvZkexbN8PKPeGLCv1cFqarzj1hBIYQQQlzo1Mg6O5bWOqu1PglcOc6xpcCNmAnjMGaF4wta66E6T58CFgC/A8RyzwE2AocnKZr/j8A24FrgG8C/a60napg0QhFjyaHjuoHnpnPsxa603Etp+Thhf3eMsgWlMJCAyhI40gaZLNSUwXTLNs2rhDdeMekhA7kCWR1xzZ4uTTKruGuNxeoqSZIKIeaGGxYqTg5AdxK21sO8qPz7Jy5cF3rMOZFiriBdP+r5U5hOnfmmM0+yA/gQ8IFRr/8RJps8Y47j/E/e06eBBbZtW47jFCf7NkNKKV6/XPHbSzVe6+z8wxkbyJ5OjoLp9D46QVrT4OfGO6rIZPSUyVGA9mNJ/jtcydFohHAmw2+fyHDT71bx6AsxPD7FbddHx5xTXuWl/aTpoeAPWISjI69TUeWjbf9wf65JGwUIIYQQc4RSytJau7ktS3pI3iHZ3HEKWAssBB7VWg/m3v8YcCfwMhAHfgR8FvicUmopJsD9V631w7lxPgr8D6bs0X+R2yY1+r601h9TSv0Z8FrgbuATSqlHgK8BP9ZaT1ZLvlixpBjl8N5BDu0ZJBT2sOmK0uF4K5OFiqj51dEH33gYqkpgbSO86YrpJ0mncPV8RXMf7O3SVATBm83y4PMDrL4mABGpSSqEuPhVhhQf3KLOa1NlIc7ERRpzjquYXexvKNJQHwV+atv2axguaH85MI8ZdKOaxAeBn0yUHLVt+17gXoD6+sK7sM/EZMnRbEbTfDCO60Lj0mDBicNwxCIY9pAYNEn7iuqxm+e72lNsf6KPdFqzfF2YZWvGKQCa53hDGfvr4vgH0vT7fbywqpb/s8bPlUt9+CwI+cZ+nqVrwvgDFoOxLAsWBcfUII149Ii/DUGP/FwkhBBi7sgFo2O2Jg09H/pdKRUA6jBBaQATu9yGiaHuxczcvxHYq5T6fcws/Zu01rtz598LvFcp9RugFBPs7s275BHgceCyoVub6J5zKwIeBB5UStUD7wT+BviiUmqJ1npgvPOKGEuKPLH+LHteMl/yVMJlz44BtlxVZt68YiUcaIXWbki7UJ97/dVmuHG9SZZOwM1q0mmNP6BItwygAh58deFxj11UpvhjGzKuIhNPg3MYlYzDjiNwz2ugurSYH1kIIWYtSY6K2WouxZwTOes1SG3b9gJBx3GmdWOO4zxm2/YKzArSVbmX7we+6DjOiWlecztjVxwMqXMcJ5s77m2YmqfXTnI/XwG+ArBt27ZZk53b8Wwfp46ZlZdtJ5JccVNFQed7fRaX3VjO8cMJgmGLBYvHzt7v3REjnTJ/N/a/EmPhktCktT7nlStKa8J4dBw34KFxYQg3lcW7qwsCHvTqijH/ISilaFwWmmBECIXN9YbOCganXskqhBBCzCYTBZxDW4ZyW4+qgGNa63T+VqLxajblZuhvAFygEtMs6F+BXswM+kHgIeALuSL524HrMGWMADYBJwGPUurvMHU6rwRCwDLMDL8373gwNdvbMdujyF17OiKYEknR3BgFx1KFxpJipNG70tz871wkCO+7FbJZeOBZ9CvNJubyeSHkJ5vRKAusUZP2/b0Znn+kh2TCpXbfKeozcZRSlN3WSOSyunHvI+hV3FiW5lvP9KA7PWxTneBPwc7mCWuXCiGEEGL6JOacWcxZzBqkNwFVjuN8P++1jwN/CXht2/418DbHcXqmGiuXCP3Emd6L4zhbpnG/rwf+FrjJcZxTZ3qt86W7Y3i1cE9HGtfVY4LXqYQjHpavm3hVqJXXIElZaspdVpXZLNcMdnAolaVEwxVJD13f7iDVYn6eiV5ZT+n19fDMPtOJ6rLlU26rqm0IAn2nn5dVTtYmSgghhJhdcoGnm/f4dDfP3HalD2EK0z8JvAc4kHdMLXAJpi7T81rroVJDfkzpoddhJnHfBvwqd+y9mJn4v827jZ2YYLgmFzhnMbP7PwQOYEoYfTd33D4gCHRhAtz/zLvmxtwYAa11cpLPHALeAtwD2MADwLu01g9N9rUqZiwphkVLvSxZHebw3jiBoMWK9WNjP21Z/GTlZmL7oDqTYOvrV3PqSIb9O/vwehVbriodUbv90O5BkgkXb1cf+vF9JFdWEayKEHu+bcIEKUDywACvDw9Cz3ESyoJaoHJsCSYhhBBCFEZizunHnBMp5grSj2OaHwFg2/ZW4NOY/f+7gY9gkp4fGe9k27YnWvE5guM4zTO90Vxjpn8Cbs51Or3g1DT4OX7Y1OasrvcXnBydjrVboux4tp9U0mXF+siY7e+j+bp6WXHqGCu0ghSU7u4n1T18X4m9PZR2HyX5YjPaheDe4/D+35r8JiwIRRTplMbyKKKlZ33RsxBCCFE0udn6NwFfBx5SSv2p1vog4MHMiA9lk2KYgPKAUqoKU4/93UAa6Ab+QCn1da31P2NmxH8N/DamyP0DAEqp3UAHsD+vVhSYbUsaaMy93gq0aq1Xjr5fpVRQaz2olPoi8GWlVDMmqL2V4Zn4LZga7mMopb6KCVT3Y2LAO7TW001oziiWFBNbuSHK8nWRCePFw73g9PnhOrObrc+C4I5OADJpzd6XY1x583CC1OtX+PoGWPjokwRaT1GabCG1dTPelZP3U/V4lKl3umoent5+uGUBbFhUnA8phBBCzGEScxYUc46r2E2aPp73/M3AU47jvBfAtu0WTC2AiYLaI0y+BHaoFGUx9lh/A0gBP7Bte+i1mxzH6SzC2OfEOruEqlo/rquZ13R2ittHy7xcdcv0t+6XhBVrS/ppjoeIeLIsnxekx1tCpj0OgH9hlI6n9tJ9yDwv7W6jLpMF78Tf0tWbojzzUA+JwSxer8XaS2SVgRBCiNknFxiqvI6cKKU8uecKs91nE/A+4CO5bU31mNpL/wWsBBqGTsXMpt85NIOvlPo94LNKqa9orZO5IDIL/HzoelrrNqVUW+54r9Y6k1tB0K+U6gYWK6U8mFpNf6aU+hPg27nzopjZ93al1I+01t9SStUBdwF/jakL/2eY8keTlTy6B2jGbKe6DbhtvHprWus7xjl3prGkmMRQclRrTXzQxe9XeH25UkZK42oYyp96c9vqXdeE5h7vyO/h8rURvM/sJehxKb20hmB/Al91lsidiya9hw2XlfKq048urWHNlsWQtypVCCGEEFOTmPO0mcSc4ypmgrQcaMt7fhXw07znzwPzJzn/0rzHCngUUx/0WLFucIjjODXFHvNcsyzF/EWzrOvnwmpqV1TDL44QrQ/iu/EqqgIhBp02VMBDZGstL794lDC9ALQGKqhW1qQZ7/oFQd7xgXns2TFIw0I/6y6duFmAEEIIcb6MV7eJ4ZpJu4DDwC+Atymlvqq13pd773bMrP13yc3sa607crPpKKUuxRS9vxwoAa7G1Ho6hImXqoFjQ9ungOO5cSoxcZkHyGBm1xcA1Vrrl5VSf40JnG/NBbBNmJo2fwUMbWX6LPAdoCOvE+hjU3wpvsWZd5qfaSwppqC15sUn+zh1PInXb2FfU8YJvDywH472QXkANtUqblyk6A2WsO+VGH6/xdotIyeo/QGLlbcugLYDuVdC8MbVEM6VQuqNmQnwUaWUKmt8XHNb5Tn4pEIIIcTFSWLO02YSc46rmAnSk8BSoMW27QCwGfjfee+XMPzhx3Ac54X857Ztu8ArjuMcKuI9zlo9Cc2JAZgXhfLghdnZLtkV575nwnR61+Dtglse62Pd60spuX74Z5nEdRvpLatEZbNkli8wW62m0LgsTOOy8buiCiGEEGdTrji9yg9GR9V1CmBm5JdhGkwOaq3vHToPE7gdwASN+zGz758E3oFJCIaBJ4BOoEkp5csFhqXA5zH1lNqAFzBborZggtVTuXM2Yuo5WZjg+ACmjlN97ryhLU9HMPWjaoFTWuvPKKV+BLwhd95zmK1Tpwt/5z7zyUK+Xlrrdxdy/CgziiXF1Hq7Mpw6br6EmZTL4T2DPBwoIeNqFpeBz6N49zqFUopIY5B5jZNMxi+thzdfCYdOweJaWJZbjPLrHfDEbrAsuPNS2Lj4HHwyIYQQ4sImMec5jTnHVcwE6c+Af8gV078D8wV9PO/9DZgvoBilfVDzb69okhlNwKv4vfVQEx7VLfThvfTe/wIqHKD6A9cRWFJ9nu52YrteGaQzZh5nXHhue4zFN2fY9UI/wbCH1ZtK2HRFGfuiXrJZzbK1kvQUQggxu+WC0qHAdClgaa33555bmMCxF+jBbFHaPrTVaOi83PakTszqx88DP1FKXY0JPB/L1Yw6BCwGKjBB5t9hAtF3a62fzl3vMmB9bva9D1Pn6RLgm5jAGMyqgbuBFZjOoEMz6z/ErAA4vTNHa70X+EzxvlozJrHkWebzW6AU5Drb+4MWvryQ02fBiO1pA3G4/yno6Ad7KdywnkxHnP7HTqJ8FiU3zsezNq+NQDpjkqMArguPvCoJUmL3Al8AACAASURBVCGEEGIaJOY8/4qZIP0L4EeYAq4DwLscx0nlvX83ptuVGGVvFyQzGp3VJIF93YqavNxhNpak7atP46azQJyTX36KRX8/7TIKZ2ygL8PLuSZNy9dFptzSX74oQjboRcUzKAX+pij3feE4J44mUUqx+Yo4d2yrY/3WwrbJHz0Q5+TRBKUVXlZtjGJNY9WpEEIIMVr+LPwUx1VprTtzj0uAfwbeiAlKjyulvoapoxRXSh3ElAT6c6313+eNEcXMxHdorXdiEnsrtdavKKW+D7wduAHTbRPMTP+VDG8zrwd25gWqb8CsqOwFolrrXqXUCWCo2+HQ53omd78vAQxtU9JaPwU8VdAX7NyTWPIsi5R42LC1hKP744SjHlasj1CWVDx4EFwNty8ZFWM98iocbTePH30VvbyBzvuPk+0135Zsd5Kqd+b1XfBYEPJDPPdti86yclBCCCHEOSAx56yPOcc1eVvyAjiO0+E4zrWYLHSF4zgPjDrkzcCnChy2qPUEZqsav0tyZw+J5zpJ7uyh2jeypEQmpXEzw6+lE+OVnCi+XdsH6O1KE49l2fm8SZROpqkxQObWRg43VXF04zzWXV9J84EE8ZhrxtjeX/A9dLWn2fVCP90daY7uj3N4X/xMP44QQog5QCk1X+Utgct/PDpQVUqFcjPj+a/dA7yklFqUe+mLmE6fNwCLgH8FPobZOgRmhryTXOJOKXWpUuoJoB34CXBt7riXgflKqSXA/4/ZzrQK0y106P0qTH0ncuPdqZT6tFLq48Brge8DN2G2mgN8ldxsfG71AFrr/Vrrz2qtL7iVlmcplhSjzF8U5MqbK9h0RSn+gMXCUsUHNlv84RaLpeWjEqSZ7Mjniczp5ChApjMx8n3LgrdfA001sLwBXn/ZWfoUQgghxPklMeeFG3NOpJgrSAFwHKd3gte7JjvPtu0HR70UBL5q2/bgqHHO/tLJcyzQk2SVP0t3FCr8WQI9SagdXkIaqAiif2sL/Oolsj4/5W+2pzXuQG+Gvp4MlTU+guHJWiGNL5vRpFMurgv+gGLcUsB5mvtgcct+ruo8SDxdwoETV+JmNdmMa7ZrnUG6O5kYGZgn4tkJjhRCCDGXKKXKAJ/WuiPvtc2YukkbgVdgOEBVSq3EBIJObvvREmA7piP6V/MKzl+JqY3UqpRaBSwE/kJr7SilGjF1laqAbcD9mFn4VmBebrwjwB8CHZi6S0PZpBcx3TYXa60fUkp9HjPj/p+591/CFLRfipl1/wamY+g9mK1N3wS+B/wvrfXx3Gd7aOZfydnnTGNJcRZctxZaOqBrADYvRi2rJ7wpzuBL5q9d2B6n72ljDbznptNPtdbs7YKUC2uqwGvJTiAhhBAXDok5L96Yc7SiJ0hnoHPU8++cl7s4DzweRW0YanN1R8drXLT87o30vWEtPp9FODp1srOrPc3zj/Tguhp/wOLKmysIRQpLkpZXeXnxqT4yGc2K9WECockXHEdPdbLw1X34YnF8iSTVr+zhSG0tna0plAXzmgIFXR+gpj5AWaWP3q40/qBF09JQwWMIIYS4uCil3g/8C/CEUurNWutTubfaMEHianLBqlLqD4E/wxSs7wCeV0r9k9Z6h1Lq28BdSqntWuuhZpE1QK/WOqGU2gisBL6hlCrPjdGKqYv5vdzxQ7PmuQ41dGit2/PudWiL1U7AR64Lu9b6eUxX9iF7MR3bj+be7899xn8Z9fEHEeJcqYjCH77W1BO1TBx4+IomfhOtozSoeJM99Rb6Xx7RPH3CzJK/WKZ41zpJkAohhLgwSMw5t8yaBKnjOO853/dwvsxfFKS7M03nqTRVdT7mLx4bbCqlKKvwTXvMtuNJXNcEo6mkS+epFAuWjEwudrSmOLIvTjBssXJDxBTuzx/jRIpg2CKb0cT6syQGs5OuRK0OuCzbc4DBrjhej6JsYx2HFzcSDFl4PIr6+YXXofL6FJffWM7gQJZg2MLrK1pVCCGEEBeug5jC8FcDH1ZK/YPWuhvoxwR7lwPfV0q9G3g/8EGt9Q9zs/GfwxSjvw34EvBPmE6gdyulmoAA0JK7zhFMgPmz3LGnMMFo/p6IQ5jO6kNB6Ij9EkPPtdbNue6j9UMBbH59qlzdprcW6esjRFENPHWKwZc7SdRGeLBuIToUoA/42WHN21dPnvDck7fu93CvJpHRBL2SJBVCCHFBkJhzDpk1CdK5zPIoNmwtLeqYpRXD31plKUoqRn6rkwmX7U/2ks2Yv1PaZUzzpPbWFAN9GQDajmvT9XQSvR1ptDdAhT9O1u+jrzfLouVQ1toNymLRovE/4+BAloH+NFW1gXFXz1oeRbRM/qgKIYQ47SiwB9iB2Z70Z5htS3FgN2a7E7n3fpkLVJdgthO5wE1KKTu3henfgX9VSv0tpmv6JkwAC2aG/SjgzRW9ByA3s3+H1vpbWusOpVQMWK6U8udtbxrPmvztWdMp3i/E+dQa0/zg2Tg9Tye4NuNS3dZNIllBYJGJGVPTqHy0oAS6c6VKq0OKQOFVn4QQQojzRWLOOUSyTrNEa0xztA+aSqE+MvNZ9XlNQbSGnq40dfMCY1afppLu6eQoQHxwbIRbU+9noDdDNqOpqPHBFH+nVEWYvpoaOiMVeDyKcG05l+59jtY4eC1N3cE+2HrNiHN2On38+FttZNKaeU0B7vnIQulSL4QQYiongRBmu9CLwL8ppZ7VWv9AKbUTuDZXL2olsEEpdRcm5jmFqd/0EYZn7L+Hqbn0N5gGQBWAA6C17lFKfQb4Tq6w/v2Y7VBXAU1KqV9qrVsxW6timPrpEwar+YGqEOdKV1zz+HGN34LrFirCvunHWQ8e0LT2a+LKy/94y3l9potlVpIWSgh5FTc2Tj3WHUsVdWFFytVsrVeoKSbchRBCiFlEYs45RBKks8CJAc3XXtFkXY3HUtyzHuZFZx48zl8UZP6i8be1R0s91MwL0H4iiWUpmpaPre25YkOExKCL62rmLw5O2ejJX1/KKyu3MLi/A0/Yx9aNS7F+3UxZOovHA3SO7WL/+M+6SadM96fjRxLs2znAqo0lY44TQggh8gzkfi3QWv+lUmor8CWl1NPkOncCFiZwfAnTAXQ/0DPOdiRXKfVp4NvA3wLPYoLOoVpOP1BKJTFbkb6Oqcf0GGbGvys3xh+d5c8rxBnRWvOtXZqehPlj35WAd6wxMWZbTKOBukkm5lNZ8FQF8JQH2NOruD9SS7SmghsWKq5bqLCmkez0eRRXLwCQxKgQQogLjsScc4gkSGeBQz2QzdULzbqaQz2KedGZj3vqeJKezjQ1DQEqa0auIFVKsWFrlMP7PJSUeqmbP7aBUkWVj7IqL4m4y7zGqeuHtremaPOXo9eUAXDwhGIgU0dzcwwPLqtXz2PFqHPyGz8pBZES2XclhBBicrkAswVYqpQKaa0/oZS6HvgaJuDMYDp8PokJMk/l6kUBoJR6C/Cq1vrVXCfRh5VSjwNvAb4L9OauM1Sr6b+VUr/SWifO4ccUYsbSLqeTowDtcfP7oy2a3zSbCeqrFyheU5+FB56FUz2wcRFcvw6AWxYp7t9nkV1fSVWPpqTCrAB9uR1umMbqUSGEEOJCJjHn3CIJ0lmgsdQkLLXWKKVoLEI50tZjSV58sheAI3vjXHFzBaXlw9/ubFbz6wc6OXY4gdenuPa2SpatjYwYY+cLA3S3pwHY/mQvN95RNWmTJJ9f4fUp0klTr9TnVTyabSQeHgCPRUdH2ZgE6R131fL9L7cSi2XYsLWEhUvCM//wQgghLkiPtIwt5XLbxIe/BGzG1HjaD7wT+Dnw+0Aa2AB8Fvgt4JdKqf+LCUJt4DLg08CrmFl/F/hL4EfAk1rrgdEXk0BVXIj8HsW6asXODvN3a3OtSWoOdZU3j+E1+3fBnmPmhUd20rugjh8nqtjdqamPwrbVih/st+hLmvOqx248EkIIIS4oo+NOiTmFJEhngcZSxbvWwuFeWFymaCyd+Yx8T0f69GPX1fR1pUckSLva0xzcPYjWGuLw4tN9YxKkqYRLx6kU6ZSmpsFPJqPxjlyIOkJNQ4DKah8thxIEw4pFq4I88pNOstoPGbCOjy2RUV0f4A8+2TTjzyuEEOLCF7qiupDDd2HimCZgv9b6oFLqz4GPA1Hgaq31d5VS7wDuxNR8imJqPX0OeAJAa53J/b4HU4RfiIvKG1cottQp/B5YUGJizPIAtJo+nJQHgK7huHEAD1/Y7eVXMbPCdG0VPNRssW2N4vFjEPCMXD3a0ZpixzN9ZLOatZeUTFjeSQghhJhNCog7JeacIyRBOkssKlMsKiveVqWaBj9H9sfRrsbrs6is9Y943+uFxGCW3u4MHo9iXtPYYLajNcXB3YOgIdafxTtFUf++7gyRUouGxgChiEV3e4aScg/9vVkspSirmiS7KoQQQhTmKOAB1gG/ztVu+p5Sygt8GVNUn1wQ+zmt9T9NMpYQFy2lFEvKR7721lWKh46Cq+GmJgWLV8GBVugeYGd0Ppm9A1R5XdrrytjbDWUBTVlA8YZlQEcfZELgM+WZdj7USmp/JwS87My41JQAgxl888Ioz8Q7j4QQQogLhMScc4QkSC9SVXV+rripnL7uDJW1fsLRkbU9PV5FOqXRrsYFshl3zBjdHWnKq7xobYr893dnqKjxjzluiOu67NkxyEBvBo9XUV7tY/XmEo7sG8SyFJdcXVbsjymEEGLu6gQOAwkYUbvpPuC+/ANHF8kXYq6rCCretDJv4jsUhQ+9lqdf6GPvl/YR6DnBJcDDm5YQW1xBIgvffTXLu154FI62Q8AH266DqhKUcwASZizPqQE69vrA1QQWl1C5bSXKklqlQgghLmgSc84RkiC9iJVV+iirHH/VZqw/S7TMQyhqoRS47tjgtbrBT2+32XIVjngoKZ/8j0tPZ5ZsRqMs03Cpuy3N699TR/OeGIGIh4amsfVFU0mXV1/oJx5zaVoekm1ZQgghpkVrHQfedL7vQ8xNtm3fBXwUWAN82HGcL0xw3PXAT4F9uZeSjuNcdk5ucgoDKc1/H9T0JuGq+Yr1NYo9B1MEXJf6CMQycKMaILWoEoCjLTGTHAVIpuHZ/XDNataFunk5XYarFYs726B8njnkcD+Ztji+eqkvL4QQ4sIlMefcIQnSOapufoDKGj/NB+J4fIp1l0THHHPrG6t5tspLIuay5arSSRs0AUSiHkCTyZhJk2DEwnpmH4t+tQP8XnjLVbC0fsQ5e3fEaG1JAvDK8xkqqn1jVrsKIYQQQswyLwFvw9Qfm8oux3Hss3w/BfvFEc3eLhOzPbAfmkqhdHGUuN+DP5UlGlA0bCnj5dzxS6s9ZgZ8aHFMaQiqS6laXs4NR9tBKXpWLWKw07ytfBZWVMorCSGEEOLCIAnSOSqT1lRUe0nG/QTD1rjbn7w+i6turpz2mF4fuFlwM5osCo92yf78Jfq701hWmujPX8T6wMjecKnU8NZ+7WrSKRdT3kMIIYQQYnZyHGcngG3bY2sUXSAGh/sy4WpNIqN43ZYgj3lWkznUy+Z1EWpWlrK43dQq3VgbgdBlsP0QVJfC9evAsmDb9WZlaUmQ0vIS1EPHyPamiFxWh0cSpEIIIYS4QEiCdI6K9WdpOZgglXKJDWQ5uj/OOruk4HEGejN0d6Qpq/LR05kxq0Y9PjxeiA9qjjWnSA2YCLy/NMP8UecvWRWmqz1NJuVSvzBIaYX8kRRCCCHERWWFbdvbgTTwRcdxvjnRgbZt3wvcC1BfXz/RYUVx7QLFsQFIZjSbahW1ETNZfvMlEbgkcvq4zXV5J21YZH7l83pO7xCygLLbms7qfQshhBBCnA2SjZqjLAu8fkUqZbqb+vyFF9Dv68nwzEPdZDMay6NYZ0dJJzWDsSyWpaio99O8fgNVL+/G9fnoWLVuTIK0otrHDa+rIp1yzUpWJYX8hRBCCHF+5RKajRO8Xec4TnaaQ20HFjqO02vb9mLg17ZtH3cc59fjHew4zleArwBs27btrDZ6aCpT/IkNyayi5AziQCGEEEKIi4kkSOeoSImHpuUhTh5NEghaLFpReAH9zlMpsrl6o25W09qSpKrOh9aaYNhDIGCRXdfI0QWmWP/CpaFxx/H6FF6fbKsXQgghxOzgOM6WIo3Tl/f4sG3b/wVcBYybID2X+nszWJaipERiMCGEEEIISZDOBlrDL1+CQ6dgSR3csskUwZ8B19Xs3RGjpzNN3YIAS1aNTIAqS5FJa7o60gRDFr5A4dcrq/QNF+tXikiJl4G+LK4LyYTZun/tbVUcP5LA61PSoV4IIYQQc4pt2w1Aq+M42rbtSuAW4M/P822xa3s/R/fHQSnWbI7StHz8SeyC7D8BQ40577gUastnPqYQQgghxDkiCdLZYMcReHqveXyqB+rKYdPiGQ3ZfCDOkX2DAPR0pikt91Jd7z/9fk9nmu1P9pKMuygFT/6ii8UFriKtrPFx6XVldJ5KUVnjR2vN4ECavh4Xnw+yaY0/YLF4ZeGrU4UQQgghZivbtt8O/CNQAdxp2/bHgVscx9ll2/angBOO43wJeCPwftu205i4+5uO4/z4vN04kM1qkxwF0JpDewZnniDNZuH+pyCVMc9//Dy89+aZjSmEEEIIcQ5JgnQ2iKcmf34GEvGRTVWTiZHPB/oyDPZnyaQ1KOg8leZMVNf5qa4zidf9O/vp6TRjppOK5oOJM7t5IYQQQohZzHGc7wLfneC9v8h7/AXgC+fqvqbDsiAQtE7HhqGwNfNBXQ3pvLKsiZnHskIIIYQQ51IRIiIxYxsXQW2ZeVxbZp7PUOPSEIGQqSlVUu6lbr5/xPuRqIdAyMLrU/h8FqUVM68/1d2ZQSmF16vweCA+ON3+BUIIIYQQ4lxQSmFfW07NvAD1C4NsvLx05oP6vHDTBlN6yeuBmzfOfEwhhBBCiHNIVpDOBuEAvO9WGEhANGim9mc6ZNTDtbdVkkxkCYU9WJ6RNUZLynysv7SUky0J/AGLzVeVzfiaS1ZGKK3wEh/IYnkUKzdEZjymEEIIIYQortIKL/Y1M4/9Rrh6NVy6DCxlEqYTSJ+MgaXw1UkJJiGEEELMHpIgnS0sC0qLGyia7vDjf4sDIYurbq3gyN44wbDFqo3RGV+vut7PW+6dx8vP9lFd7+OKmypmPKYQQgghhLhABHyTvt3782Ziz5wCoOSG+ZRcN+9c3JUQQgghxJQkQTpLtMU0zf3QWAK1kZl1sJ+u2oYAtQ2Boo65ZGWImnofwZAHn18qOAghhBBCCNBaE3uu7fTz2LOnJEEqhBBCiFlDEqSzwMkBzdde0WRcjddS3LMeGqLnJklaTNms5vlHeujuSOPxKuxry6is8U99ohBCCCGEuKgppfCW+8l0JQHwVBR3kl4IIYQQYiZkid8scLAHMq4GzO+Hes2K0t80u+xs1+f57qavpyNNd0cagGxG03xAutgLIYQQQsx1fd0ZTrYkiL55GcmGEpp1gObGGpJx93zfmhBCCCEEMEdXkNq2/QngrUAWUMBnHMf53vm6nwUlZlZda41SilK/5us7IZHRgCaesbi0YfavKA2GLZSl0LlkbzAs+XchhBBCiLns1PEk25/sA63xByySZZWocqBHs+vFATZfWXq+b1EIIYQQYs6uIP2C4zgbHMfZDNwOfNW27fPWUWhRmeKuNYqrF5jfQ16VS44azf1nZxVpPJblwK4Yxw4n0Hrm14iUeNl0eSnVDX6alodYvla62AshhBBCzFbdHWl6OtNn9Ronm5OQizMH+jPEY9nT76WSsoJUCCGEELPDnFxB6jhOb97TKKA5z8nipeWKpeVmlWgspYn6FQMpE0wuryj+6tFMWvPMwz0kBk2QGo9FWL5u5gnN6no/Xp8iFPHg8c7+Va9CCCGEEHPR7hcHOLJvEIDFK8Os2hQ9K9cprfBystk8DoU9lDf66GxN4fEqlq4On5VrCiGEEEIUak4mSAFs234f8GFgIXC34zidExx3L3AvQH19/Tm5t4hf8d4NsLdLUROCxeVnlmjsPJWipytDTb2f0govR/fHOdmSpKzSS0Nj4HRyFKCrfearB0zStZv+nsz/a+++4+wqq/2Pf/Y500t6TwhJSEIJzbAoAmIQUcSKBX8gIHIVr4rXgggWBMWCeO0U9d6LiiAqiICKiChV6qJD6CUhkJA+mUky7Zz9++PZE04mk2QmTGbvM/N9v17zmtnlnFlnZ2bynLWfZy1yuYjXHTyMcRNVgF9EREQkaxY+u36jr7dXgnT6zrXkcrC2ucCkHWsYOaaSdS0FKiojqqqH6mI2ERERyZpBmSA1s/uBqZs5PN7dC+7+M+BnZrYHcJmZ3dhTktTdfwH8AuD4448fsI5Jw6sj9pu47Y9furiNf169gtZ1BYaNrGC/eSOYf38zAKuWtVNVFVHXkGddS0iSjpnw2rvNN63soHl1JwDFYsxLz7cpQSoiIiKSQfWN+Q3jtoZhFcRxzJrrF9L6+GoqJ9cz4qjp5Kryr/n7RFHEtNkbzxSta3jtzysiIiLSnwZlgtTd5/bh3EfM7GVgHvDH7RbUAHvyobUsW9wGQEtTYcMSqi7t7TEHHDaSxQtbqanLM2HKa09k1tbnyeUjioWQR24YpsGviIiISNY0reqgpjbHyqUxa5Ob5U/csJThdy8FoLCmnbUT62g8ZFKvn/Plha2sXVNg4g7VNAwflG8xREREZBAbkutazGy3kq+nA68D5qcXUf+rrIqAsDQ/ysGY8VU0jgiD1aqaHDvMqKG6Jse02XX9khyFMBtgn4OHM3HHGmbOqVddKREREZGM6ewocu/NTSxb3M6yJe20tYZGSU8/1cr6zlfPizt630BpwdPreejONTzz2Fru/NdqWtcXtv4gERERkQwZqrd3zzazOUAHUAD+y90fTzmmfrXr6xp46YVW1qzqZNykambsUs/sPSPWtRSorctRUbl9cuNjJlT1y3J9EREREel/ba1FOtqLFGPoaC/S0R72V4ytoap+GCxcQ8W4Wur3H9/r51y1/NVa9p3tRVqaCtTUaiWRiIiIlI8hmSB196PTjmF7y+VCjdE4htq6HFEO8vmIRi15EhERERmyauvzjBhdySP3NtO6tkiUg/a2mN3mNrDD7uOJOwpElSG5WSzGrGspUF2To7Jq8zfXx06sYvHC1rARwarl7UQ5GFEX0bmilcoJdeSqlTAVERGR7FK2bJB66YVWiKG+IU9ba5GlL7czZXpN2mGJiIiISIpyuYjpu9Ty0oJWxk2qoromx5TptczavR7g1eRoIcZva2LFK+1UVOXY95DhjBhd2eNzTp5WQ3VtjhWvtPPs/HU889g6ivesYccFrzAq6qRiVDVjProrubqeHy8iIiKStiFZg3QoqKvf+C59XYP+qUVEREQE6hoqqG+soLomjA9r6zcdJ65a3sGKV8L6+872Ii88tX6LzzlmfBWNwytY21xg2ZJ2mp9vZmVzzPLWiFcWt7P+6ab+fyEiIiIi/UQzSAepHXaqob29SNPKTsZPqmLUWNUFFREREREYNqKCPfdr4LH7WqioiJg4ddOGndU1OYgiiOOwXbv1m+3tbUVeeamNYjEmv7pIRVvMknwFRLDkwTZevmkRxPCGt41kxi71/f66RERERLaVEqSDVBRFzNxNA08RERER2dT6dUU61nay5tGV3PjPlzj4LcOpP2wH1jYXGD6ykobhFey5XyMLnllHx8p2qgodPHJPM5XVYYxZURlt8pztbTFTptewtrlA1ZQq2l/MU0ORirE13HffWiauWkO+vcDNy9Yz49uzUnjVIiIiIj1TglREREREZBDr7IhZvLCVfGXExB2qiaKIlUs7aF/QTLG5gzZgwW3LeWVpJdRXUt9YwQGHjWDSjtUs/s3TdDy4nEdWFWjbfzLR9OEse7mdg94ykpY1BWrqclRVh9ml4yZV8fwTeaprclRU5qicVM3KZZ2sXNXBiGeWM6ylBSKoX7WWzvXTqajVWxERERHJBo1KREREREQGqTiOufeW1axa0cHyxe3U1ueZs08Do8dX8lJyTk0+prkjR1NTJ6sXd5LLRUyeVs2kUTnWPbSCYgzFQpHlTzRTqKyh6cV1rL/iKao6C8S7jWHux2cwfGQlI0ZXcuBbRtK0ooORY6sodBb5y2+XUlEZMb62SGF1TGVVjkmTKmFdJyhBKiIiIhmhzj0iIiIiIoNUR3vM6hUdNK/uZPXKDpa/0s6Cp9dTXZNn9pvGEtdXUVERUbvzcJatjmlvK9K2vsiCZ9ZTNbyCivo8uRzkq/K0Vocu9KMWrGTds80UVreR88UsvK+JQmfMw/es4aG71tCyppNcDjo7YeSYSkaPq6Jhn7FMnVXL3gcOY/obx5IfuWndUxEREZG06LatiIiIiMggVVkV0TC8glUrOgCorQvzI9rWF1i0uINh+46hszPmqZUdtLW2U1UdMX5KFVXVOfK1lez2xd1ZeNVCRtZVEI0eRQEoPlukrbVITExlZY4JFTG3/30lj93fQkdbkc5CzLRZteyyVwONIypoXt1JYdYoJh8+jtHDI6pnDCPKbVrDVERERCQtSpCKiIiIiPSBmV0AHAa0AS3AZ9zdN3PumcCJyeav3P2cAQkyEUUR+80bwdgJVTz5SAuVlRGNIyooFGKeebCZfKGTXGM1xTjUJ125tIOaujw779kAwIg5IxkxZyQAjU+u5V/XrmT1sOFMrl1LRJH2nUZSP62BBdesoKWpg5XLOsnloaGxgidoYeToSqIoYo99G5g8rXYgX7qIiIhIr2mJvYiIiIhI3/wN2MPd9wK+A/y+p5PM7BDgA8DuyccHkn0Dqromxy57N3D4UWMYPqqSzo4iD924lPEvLaDh6RfonP8yo8dWUFUdMWJMBXvtVk2lv0zT3xdSXN8JwIql7Tx8TwtrmztpifLMnzmZJ/bckcpDplBVFVFZlWPN24g/oQAAIABJREFUqgKFQkw+F9G2vsAri9poXV8gjmOefXz9QL9sERERkV7TDFIRERERkT5w97+UbN4JTDGznLsXu536QeASd18PYGaXJPtuHZhIN/bEQ2tZs6qTYjGmZf5SxlVB7ehKJuTXsWbtehatyjNseAULL5jPjhMrqKiIKKxsY9Qxs2hpKpDPh+cpFiFflaO6oYL6hjyNIypobytSU5ejojKifljYNyKZPQpQ6IzTeMkiIiIivaIZpCIiIiIi2+4U4K89JEcBpgILSrYXAjsMSFQ96OwIScpcLmLSiAKVUYGGqgI2qolps+vYcVYtwxsjiqvbaG8NL6djaZj5OXZSFbV1eSbuUENNTY7JO1Yza/d6chURt/x1JWtWdTJ5Wg2jxlaSy0WMnxyOR7mIXD5i573q03rZIiIiIlulGaQiIiIiIiXM7H5CcrMn4929kJz3/4BjgX5ZNm9mJwMnA0yYMKE/nnIjM+fU0bSqk872IlPeP4c5K56FpU2w91yqOhp48d6VFIsxcU0dw5o6WLa4ndqDRjCmM6auPs9Bbx1J08pO9ty/kYXPtFJZHZGviCgWYurqczSt7IAoYseZtUQRrGspcPhRYyCCfD7i5QWt3Hd7E0tfbmfS1Gr2nTeCMeOr+v11ioiIiPSVEqQiIiIiIiXcfe7WzjGzo4BvAYe5+yubOW0hsGPJ9lTgxS18318AvwA4/vjj+31N+qixVbzpXaMpdMZUVeeI47m0PdNElIsoLIMJU6ppby/SPmEKC5vWUzOsgtWj62h4ah0zd6unpjZPzeQ84ydXb2ji9MAdTbSs7mTi1GqGt1SQy+WorQ+L1HL5kEAFiOOYR72Zlxe00tZa5PmnilRU5Xjze0ZvWIYvIiIikhYlSEVERERE+sDM3gH8ADjc3V/YwqlXAD9Jut4DnAB8ejuHt0X5fEQ+HxKSq696jvWPrASgOHU09Y0N1JOnZU0BJjdQrA6Jzs72zedqZ+/RwLqWIq3rCuy8VyOVlRHPPr6O6pocu+7dsOG8KIqIchHFQrJNWPIfFyHKb5/XKiIiItJbSpCKiIiIiPTNL4F24Eoz69p3mLuvMLP/Ba5192vd/WYzuwp4LDnnEne/JYV4NxEXY9Y/unLD9vhlTdTOHUdLc4G9DqjmxedaWbm0ndqGPHXDcjx45xoahuWZtnMtq5Z1UNeQp76xgvrGPAe9ZeRGzz11Zm2P33PP/RpZ29zJkoVtjJ1UzS571pPLa/aoiIiIpE8JUhERERGRPnD3sVs49tFu22cDZ2/nkLaosLaDNTe8SHFdJ42HTKJqhwaiXETluFo6XglNmKon1LJLyYzPiVOraW+LaW8rcMcNq0Nt0hhu/dtKWtZ0UlGR44ijxzBjl943Xxo/uZr3nDAhzByNYyqr1C9WREREskGjEhERERGRQazp2hdY/9AK2p5uYuVlTxF3hHXuo46bTf0B42k4aAIjj5650WOiKKK6Jsf6dUWKxbDEfs3qDpYsagWgs7PIfbet2aZ4KiojJUdFREQkUzSDVERERERkECs0tW/4uthaoNhaIF+ZJ99YxfAjpm7xsaPGVNIwrIKWNZ2sW1ukdV1MZ0cnjcPz1NareKiIiIgMDrp1KyIiIiIyiNW/fjzkQq3P2j1Hk2+s6vVjKypzvP7NI9lj30bGjKtk+s61VNfkqK3Pc+g7R2+vkEVEREQGlGaQioiIiIgMYnV7jaFihwZamzqpn1rX58dXVEaMHl9FZXWeCVNyTJhSzdhJ1dQ3agapiIiIDA5KkEq/WvFKO4ueb6W+Mc+MXerUmVREREQkZa3rC9x951rWtRQY9nQH+x86nIrKVxeSLVnUxpJFbQwfWcG02bVE0abjt9r6PLvuXc+zj6+juibHrnv3vjmTiIiISNYpQSr9Zt3aAn5bE8VCKOQfxzBrdw2eRURERNL04rOtrGsJjZnWrOpg8cI2dtipFoDVKzp44I41EMcsXgAVFdGGY91Nm13HtNl9n4EqIiIiknWqQSr9Zn1LYUNyFKBlTSHFaEREREQEoKp64yF/Zcn22uZCuKud0PhNREREhiLNIJV+M3xUJY0jKmhe3UkuFzF5enXaIYmIiIgMeTvsVENLcyerlnUwblI1E6a8OkYbM6GKmro8resK5CsiJk7V+E1ERESGHiVIpd9UVEYc8KaRrFreQV1DjvpG/XiJiIiIpC2Xi5gzt7HHY9U1OQ56y0iaVnZSPyxPXb0aL4mIiMjQowyW9KuKyoixE6vSDkNEREREeqmqOqfxm4iIiAxpqkEqIiIiIiIiIiIiQ5YSpCIiIiIiIiIiIjJkKUEqIiIiIiIiIiIiQ9aQTpCa2TwzK5jZKWnHIiIiIkNDFEUfjKLogLTjEBEREZHBS2POvhmyCVIzawS+C/wt7VhERERk8IuiKEq+/G/g5CiKhuw4TERERES2D405t81Qvkg/AL4HLE87EBERERm8oiBfsut6YDKgtuEiIiIi0i805nxthmSC1MzeBgx39yvTjkVERETKQxRFY6IoOjaKor2S7VzJsXwURRUld+w3iINCcl41cC8wB2gcoNBFREREpExozJmOirQD2B7M7H5g6mYO7wycCxzey+c6GTgZYMKECf0Sn4iIiJSPKIqiOI5jYBbwVmAxQBzHxa5zugajm3n8TOD/gP2AfwHPAQ3AOGDZ9otcRERERMqFxpzpisK1HzrM7GDgKmBdsmsM0Ab82N2/sZXHLgMW9OHbjUFL+PuTrmf/0bXsX7qe/UvXs/+81mu53N2P6K9gyklyVz4CYiDXfTBaMoAliqKxwFHAPKAD+FUcxzclx3LA1cAI4NNAHvgh8AbgiDiObxiQFyRlZxvGnQOlXP9Gl2vcUN6xl6Nyvd6Ke2Ap7v43JMedGnNmx5BLkHZnZr8C3N3P3w7P7e5u/f28Q5WuZ//Rtexfup79S9ez/wzVa9m15Cju5SAnqdVUJIyLips7J47jQhRF+wM7xXH825LHXgjsATwCrAb+E/gc8GtgPPACcGQcxzcmj9kRuB/4ZhzHP9zW1ymShnL9u1KucUN5x16OyvV6K+6BpbgFNOYcbAblEnsREREZunoapJbcnaf7gLTkTn3X3flRhFpNbwAeA74PzIuiaAzwMWD/KIr+HsfxCuAUYG/gvXEcv5Q8/sVk/78JhfEXAWuSYxVxHC+IougxYI+uQXB/vn4RERER2f405hxchnyC1N1PTDsGERER6R9RFDUChwDVcRxf1bU/GcD2eHc/iqITgKeBNwGnAVcQljZ/HngCuAQ4LY7jlVEUPQrsCkwDVgDvJwxoD4yi6LDk2B5APaHeUxuwFJgN3FMSw7PALkAd0Nw/r15EREREBoLGnIPPkOxiP4B+kXYAg4yuZ//Rtexfup79S9ez/wzaaxlF0Ygoinbo4dCZwJ+Bb0VRNKLk/NlRFB0XRdHpURTtnuzrGgedBVwH7Eio6/RZ4D7CHfgFwP8ADybnLgSqgOnJ9rPAicCphBkA1xAGsLPjOL6NMFBdA7wZwsyBJO49gGGEOlEi5aRc/66Ua9xQ3rGXo3K93op7YCnuIUJjzqFjyNcgFRERkfISRdEngAsInTnnxXG8KIqiyjiOO6IougGYQRhUfg6YD3yPMAhdTri7Pho4N47jXybP913CXfwPxnF8RbJvV+BK4Lo4jk+LoqgqjuP2KIr2AX4CXB3H8feiKPoWcAyhRlRcEmME1MVxvDaKomOAy4CzgWuBI4DDgUOBA+M4vmv7XS0RERER2RYacw4tmkEqIiIi5eZ5QsH5GcDRSY2ljiiKJgJrCYXr2wh33KsId+KPjON4nziODwQuBT4VRdEuyfM9RVhy9GTJ91hFWOo0NdnuqiG1CGgCZibbfwDGAucknUWJomgS8AngeIA4ji8n1Ic6hlAj6k2EGQD/mXxvEREREckejTmHkCFfg3R7MLPZhC5iowm1Ik5w96fTjao8mNlo4DfATkA7oT7Hx919mZkdAPwcqCV0ZzvO3ZemFWu5MbOzCHeS9nD3R3U9t42Z1QA/JCxdaAXudPeT9Xvfd2b2DuAcQhHzCPi6u1+la9k7ZvbfwPsIdYn2cPdHk/2bvX6D6No+DywGrgeMsLzod8BeyfE/AycAs+I4vjqKokuB2iiK3k5YZvRWQiH7vQgD0vsJ9ZtqS77HKuBFYH+AOI47k/3LgCXAuCiKojiOH4qi6DTCcqd9oygaR6gD9TxwdnJOHMfxhVEUXR7H8aqS76FlbiIiIiLZpTHnEKIZpNvHz4AL3H02YTr2z1OOp5zEwHnuvrO770Gos3GumeVI7r4k1/VW4NwU4ywrZjYXOIBQ1wRdz9fkPEJidHbyM3pmsl+/931gZhHhZsjx7r434a7nr5OfTV3L3rmaUBh+Qbf9W7p+g+XaLgZqCAPN24Ezkv2VhMHolYS7+V133CcRBrPfAg4C7iDcnd8xOf4wYUy0ob5UHMdthCVTw6IoGg6hflTSjbSFUBh/cnLuz4B3EgbJFwBvAw6J4/jG0iVQ3QaqIiIiIpJtGnMOIZpB2s/MbBwwl1DnAeBy4HwzG+vuy9KLrDy4+0rg5pJddxGmjO8DtLr77cn+nxFmPZ40kPGVIzOrJvzxPIZXr62u5zYwswbCHcIp7h4DuPsr+r3fZkVgePL1CMIAZAy6lr3S9ftrZhv2belnkTBLd7Bc27XAOkKB+kuBz0RRdBxhZuyDcRyviaLoFWBicv65hJ+tD8Vx/BhAFEUfAqZFUVQXx/G6KIpeBmZFUZSP47iQPG4FYQC7G3AnYUBbBK4CbgFWdwUUx/EThMGzyKBhZh8gzDppBz7t7n9IOaQ+M7M6YAol73vcfX56EUmWmFke+A931+wq6RUzOwzY1d3PN7PxwHB3z+zS5WTywe7u/nDasZQpjTmHEM0g7X87AC+5ewEg+fwyJXcIpHeSP+afIBQXnkrJLCl3Xw7kzGxUSuGVk28Al7r7CyX7dD23zU6E/7zOMjM3s5vN7GD0e99nSYL5aOAaM1tAmA15ArqWr9WWrt+gubbJYHIR4W9ZC/Bd4FPAyYQlUAAvAWOjKKoAZhPu4D8BEEXRRwjdPMcSliZBWN70RsIsgS73Ap8n+XvZteQpjuOb4zj+YxzHLdvpJYpkxTmEGTBHAt82s6+a2e5m1mhm07fy2NSZ2aeAV4B/AH9NPv6SalC9YGY5Mzsy7TiGguT/wpPTjmOoMbPPm9nw5OvfmNkTZvaWtOPaGjM7g9CF/DPJrkrg4vQi2jp3LxISe7INNOYcWjSDVLLsp4Q/QucTOsFJH5nZ6wm1Us7Y2rnSK3lCge4H3P00M9ufsLzhA+mGVX7MrAL4EvBud/+3mR1EKDx+fLqRSRl5kDAjdnocxxdHUXQ8oXbTC8nxhYQ3LpOBawjF6fNRFLUSZizfkxwblTzmS0AHsL7rG8RxPJ/QkVRkqGrrmm1pZm8Afky4mfV5wu/UO1KMrTdOJcyc6l6KJNPcvWhm3wSuSzuWIeImM3u/u1+ZdiC9ZWbnbem4u39xoGLZRie6+w/M7FBC0ugkQrfuG9INa6uOIby3ugfA3ReZ2bB0Q+qVZ8xsWrcJM9J7GnMOEUqQ9r8Xgclmlnf3QrJsY1KyX3opaT4yC3hnMkhcyKt1OzCzMUAxWZIvm/dGQs2S55NluFOAvxMGILqefbcQ6CQsTcbd7zaz5YT/3PR73zd7A5Pc/d8ASZJ0LaG+q67lttvS/0HRFo6Vo/mEmxYzCfWqPwVU8+rgchHhbv1hcRyfE0XRSsIbmxWEJM9DwPpkqVMUx3HmZ5WJpOAlM9vH3e9z98WEmf9dyuF3Zkm5JUdLPGhm+7n7PWkHMgScCJxqZusJy2kjIHb3cVt8VLrWph3Aa9S1rPhQ4DJ3vyNZPZh16929o7S8EaGHRtY1Ag+b2e2ECUgAuPvRm3+IlNCYc4hQgrSfuftSM3uQ8AtxafL5gTKs75YaM/s2oUbm2929Ldl9H1BrZgcndff+E7girRjLhbufS0nzJTN7gTDbYz5wsq5n37j7cjO7iVDD8YakI/g44CnCnUX93vfeImCKme3s7k+a2a7AeOBpdC232db+Dxpk/z8tIAxWdwX+ntx5J4qiKDn+APBl4G6AOI4vINRj3kRpUXsR2ciHKe/3C/9IZtr9jnADDiibGqT7AP82s6fZOKGxX3ohDVq29VOyxd2/nnYMr9F6MzudMBZ5Q9K8syrlmHrjxaS8VpwkdL8MPJZyTL1xKVpm/1pozDlERPr36X9mtgvwa2Akob7ECe7+ZLpRlQczmwM8Skg4dU05f97djzKzAwkdl2sIU9OPc/dXUgm0THUlSN39UV3PbWNmMwi1hkYTlkZ8xd3/pt/7vjOzDxHKPxSTXWe5+9W6lr1jZj8B3gtMAJYDK9x9zpau32C6tlEU1QK/AW5MOnqKiGzEzJ7vYXfs7jMGPJg+MrM39rTf3W8Z6FiGgmSZ9Ex3vz/tWPoqqd25NyX1DN39G+lFtHXJJINPAre6+1VmthNwtLt/J+XQtsjMJgCXAPMI49fb0HuoQU9jzqFDCVIREREREREZkpKGWD8HCu4+zcL66bPc/Z0ph7ZVZnYusC8wh1D78N3Aje5+XKqB9ZKZjS3HlSxmVgfk3L0sGucktf9PYtNE+kmpBSWSQeW8ZEZERERERGQjZlbt7m1JEmMT7r5uoGPqq6TD9+lsmtB4U2pBDV5fJyQZ/wbg7p7MaCwHbwdeB9zn7h83s28A/5NyTFuVNDr9A5ADdkiS0ie7+8npRrZlZnY88Bd3X5VsjwLe5u6XpRvZVv2ckPs5FLgIOBa4NdWIRDKoHAohi4iIiIiI9NadyecWoDn53FKyXQ4uJjSymU1IeBVIOmdL/3P3Jd12tfV4Yva0unsnoSZmpbu/RGjKmnU/AN5GKBGEuztwUKoR9c4XupKjAEmD2y+kGE9v7efuHwZWJ2UMDibMOhaREppBKiIiIiIig4a7z00+l/NkkJnu/j4ze7e7X25mVwE3pR3UINVsZuNJupGb2TxgdaoR9V5zMlP6DuDXZraYV/s4ZFmVu8/v1g2+Pa1gXqN82gH0QtfPRMHM6ty9yczGpRqRSAYpQSoiIiIiIoOWmVVR8r6nHJbY8+oMxvZkGe8qYGyK8QxmZxCW1083s5uBWcC7Uo2o944hzC7+AvB5YATwgVQj6p02M2vg1aT0bkBruiH1yhIze6+7XwVgZu8DlqYcU2+sNLORwPXA38xsOfBSyjGJZI4SpCIiIiIiMuiY2fuBHwKTCYmYKPlcDjO+nkoSo78F7iLMaLwv3ZAGJ3e/x8wOBQ4k/Izc4e5lMYO0W/f0b6YWSN99C7gBmGRmvwKOAMqhsdRngGvM7Lxku5PQGCvr3u7uBTP7CvAhYDhwScoxiWSOutiLiGxnycBvjLu/I+1YREREhgozew74IKGBTTHteLaVmb2BkNC4Pqk3KdtBOc40NrOdga8CO7Fx7PulFlQvmdkM4K2EpPQN7v5MyiH1ipnlgZ2TzSfdvZBmPCLSfzSDVGQQ2VwiLukMeS8w3d1fSCE0ERERkYG22N3vTTuI18rdb0s7hsHMzN4L/ASYmOwqp5nGvwOuAH5JWGpfNtz9OUJH9cwzs2p3b0vqvQK8kHyuNrPMJtPN7F6SMgY9KYdEushAUoJURPqNmVW5eyYKrGcpFhEREUnFT83sHOBPlNQ3dPf56YW0ZWa2jJ4TGhEQu7saq/S/84CjgbvKcKZxzt2/nXYQvVXGCbs7gblACxvHn/Vk+hfSDkCknChBKjJEmdkhwPeAvYAmQo2r07uSikmR+kfd/ZSSx/yKkhmqyTmPA2uBDxPupu5rZh8HTgWmEgYS9xFq32yyLMzMpgHPE+rhfBKw5Hn+y91vKDlvtyTeQwidGP8JfM7dl5TGBtwGfBqoAjZ5E5F09/ycu/8u2b49uQYj3b3TzGYCTwM7uPuiZLnVOUl8o4DHgK+6+997G1sPMexFKJJ+sbt/padzRERE5DWbTGhc82FenV0XAzNSi2jrbOunSD9b6e53pB3ENrrTzPZ094fTDqSXyjJh5+5zk8+5tGPpC3e/Je0YRMqJEqQiQ5CZTSZ06/wNcCKhbtH/AkVCYrMvjgN+AbwBiJLl/BcQ3ozcTuim+aZePM95hDcxDwOfIhRAn+nuL5nZROBW4P8IA6tKQnH3a8zs9SV3+99ISPYeQbij25NbgHnA75JlMvsCzYQ3JHclx55190XJ+b8kXJ9jgUXAkcCfzWxfd3+oD7EBG+qIXQt8w91/2IvrIiIiItvmv4CZ7r447UB6y90XpB3DUFGyXPpPZvYJ4PdsPNM4k8umu9kf+IiZPcnGsWdyJma5J+zM7EzgV+7+Ytqx9IaZXcGWZ+wePYDhiGSeEqQig88RZtbSbV/3u52fBF4GPpkk8B43szOAn5vZmX0cED7v7huSqkkdp7XAte7eDCwAHurF81zk7n9InuMzhKLtnyAUnv8E8JC7n17yfU4AVhISm/cku1uBk9y9bQvf52bgc8nXBwLPAXcDh/JqgvTm5HvsBBwDTHP3hcljzjezNwMfJ1zH3saGmb2DMFP3FHdX50gREZHta0E5JUehrJcgl6Ou5dJdN9UvKNnO8rLpUp9NO4BtYWbDgdOBvYGarv3u3ptJFWkaBtxlZo8TJlH80d1bt/KYNP0l7QBEyokSpCKDz63Ayd327U6ov9VlVzats3Q7YVn6TMIszt66r9v2PwhJ0efN7O/ADcBVSbJ0S+7s+sLdi2Z2N7Bbsmsf4JAeEr8QZnd2JSEf3UpyFELy86Jk5uc84Kbk8ccA3yHMQv1Scu5cwiB5fpgYu0E18K8+xrYP4d/gWHe/YisxioiIyGt3j5ldTmhiUzq77rr0QtqqslyCXI7Kbbl0T7pmZJpZfbK9Nt2Ieu1iYD4wGzgTOIlN31NkjruflkwqOZKwWu4HZna1u3885dB65O6/TjsGkXKiBKnI4LPO3Z8p3WFmI/rw+K5ZC0U2XaZe2cP5Gw3E3L3ZzOYS6nEeTkg2fjtZkv5yH+IolQP+Ss9vGl7ZXCw9cfcnzGwJYcboPODHwL2EmaG7AlNIZpAm3zcmLMPv6PZU6/sY2/PAUsIyqGt7kcgVERGR12af5POnS/bFQGYTpN2XIJdh4qvsmNmP3P2zW9uXRWY2g7A6aW8gNrMHgOOSDvFZNtPd32dm73b3y83sKsKkhcxz9wKh3NbzhPH/SYSVZZllZmOA84HDkl3/AD7j7svSi0oke5QgFRmaHgeONrNcySzSg4F24Nlkexkwsdvj9iI0UNqipBnTv4B/mdlZhMTgOwi1SjfngOQxmFkE7AdcmRy7n9BddIG7d09UbotbgLcTlsDf7O7LzGw58EU2rj/6ACFJPMHdNzdo621sK4F3ERo4/cnMjlKSVEREZPtx90PTjmFblXHiqxwd0sO+Nw54FNvm54Tx9S+T7ROTfYenFVAvdY2B281sFLAKGJtiPL2SxHos8BGgAfg12W761uXnhEazpxLe23ws2ffeNIMSyRolSEWGpgsJNYsuNLMfE/5jPxc4v6T+6L+AH5nZu4AnCXdGd2ArCdKkzuZOhKX+KwkzNRsJSdkt+YSZPQU8QqjtuSNwUXLsAsJ/5L83s+8SkrczCInJU3uxfL+7m4GfAk+W3Dm9mdBwasNSFHd/yswuA35lZqcSkqGjCDNPn3P3q/oSm7svN7PDCNf2KjN7r5KkIiIi/cvMjtzS8Ywvse9SromvsmFmHyCM16aZ2R9KDg0HyqFBE8BYd7+4ZPuXSS3/rHsqSTb+llBmq4kyWGJPeE/0J+C/3P3faQfTBzu5+/tKts8yswdTi0Yko8q+7oqI9J27vwS8DXgd8CChDtDlwJdLTru45OPfhE7vf2LrVgPvAW4EniAsPfmou9+2lcedQehi/xChC/1RXTM5k6X5BxGW/V9PuAN6AeHu87YkGG8m3CC6eSv7INwh/iVwXvJ6/kKYabBgW2Jz9+XAmwjJ5j+aWfU2xC8iIiKbd1rycSZwLfCt5ONaQvPHcjDW3S929zj5+CVlMMOuzDxFGNc1J5//Qiib9DPCOLkcFM1s564NM5sNFFKMp1fc/Th3X0l4n3EZcA3woXSj2jIzywNfc/eTyyw5CpAzs3FdG8nXygWJdBPF8WabJIqIbHdmNo1Qn3Nfd/eUwxEREZFBImnQ9CN3vzvZ3o9Qdy/TiRgAM7sfOMbdn0y2ZwOXu/s+W36k9IWZHQx8A9iDUJ/2YeDrvbixnwlmdgRwCWHCA4RyWMe7+w3pRbV5ZnYp8D13fyiZQfoQsAYYA3zF3f831QC3wszc3W3rZ2aLmZ1AaEb712TXkcAZ7n5pelGJZI/uGoiIiIiIyGC0e1dyFMDd7yEkwsrBl4HbzOwGM7sBuI3Q+FL6iZm9m7CC6veE0gVvAf4IXGZm70kztt5y9+uB3YGfJB9zspocTcx194eSr48HHnf3OYSGaqekF1av3WRm7087iL5y90sIP98PJx9vVXJUZFOqQSoiIiIiIoPRWjM7risRYGYfokxqS7r79WY2B9g/2XVXUqZH+s/XgCPc/bGSfQ+a2W2EWZlXpxNW37j7UkJ5gHLQWvL1wSTlu9x9kZmVw9LWE4FTzWw9sJbQ8Ch293FbfFRKzGwKodmsJz/njyX7zcwmJ2XXRCShBKmIpMrdXyAMLkRERET600nAJWb2v4Tl048QujhnVpIUnejuNyaNJP+S7H+zmb3s7vPTjXBQqe2WHAXA3R81s5o0AuotM/unux9mZssIP9tdMp2wAzCzSYSu9fOAs0oOZfqaJ8ptef15hJq63dUmxzJfbkRkIGmJvYiIiIiIDDruPj+pFzibUH9vBKEpTJZ9B1jaw/5XgHMHOJbBrsrMKrvvTBpoZr2J5nHJZwP2Lfno2s6q7xDqpT4N3N6V8DezA4CFaQbWG+6+gJDvHxIyAAAOSklEQVTcHe3uC7o+0o5rC2a5+63ddyY1dvdKIR6RTFOCVEREREREBhUzqzCz95nZX4EHCDNHj3f3WSmHtjUT3P3h7jvd/RFgegrxDGbXEGYYD+/aYWYjgF8lxzLL3Rcnn0uTdOsJs48zm7Bz9yuAPYF3AO8tObQQ+FgqQfWBmR1JWKZ+VbJtZvbndKPaovotHFMuSKQbLbEXEREREZFBw8x+CBxDaEbyK+D9wPzShk0ZNnwLxzaZ7SivyZeAi4AXzeyZZN9M4ArKpCFWUi/1HYSl9Q8Aq83sOnc/Ld3INs/dlwBLuu17OaVw+urrhBm6fwNwdzezndINaYtazWyGuz9XutPMZrBxPVgRQQlSEREREREZXD4O3Al8x91vAiiTBjAAy8zsde7+QOlOM3sdsDKlmAYld28H/sPMvg7sQUgyPpLlGZg9aHD3JjM7DrgMOAN4CMhsgrTcufsSs41KkbalFUsvfB+41sw+C9yT7NsP+AGhBqmIlFCCVEREREREBpNJwLHA98xsFKEjebm87zkHuMbMvsHGCY0zCYlf6WfuvpAyqH+5GV21Ug8FfufuRTPrTDOgQa7ZzMaTNMYys3nA6lQj2gJ3v8zMagm1lycnu18Cvunul6YXmUg2RXFcLjdTRUREREREes/M9iR0sz8WeAK4zN1/nm5UW2ZmbyEkROcmu+4nJDT+nl5UkkVmdiGhG3wFMIdQc/LGpDmZ9DMz24/QFX46YabuLOBd7n5fqoH1gpmNBXD3ZWnHIpJVSpCKiIiIiMiglnQrfw/wEXc/Mu14RPqDmUWEbuTPufsaMxsNTO1eokH6T9LUq+tvyHx3fyjNePrKzL7v7qemHYdIFqlzmYiIiIiIDGru3uHuV5RjctTMLk87BsmsWcATSXL0rYRO8C+kG9LgY2aXmtleyWYe+B7wVeAGM/toepFtk0PTDkAkq5QgFRERERERya6d0w5AMusPQMHMpgM/B2YAv043pEFpbslM0eMJM0fnAPsAp6QX1jaJ0g5AJKuUIBUREREREckuJTRkc4ru3gG8HbjQ3U8GpqYc02DUWvL1wcCfANx9EUnDpjJyfNoBiGRVuXRzFBERERERGYoOSzsAyayapKv6O4GvJPuUUN8OzGwSsIrQFOuskkM1qQTUB0mt2pOA2e5+uplNAya5+x3pRiaSLUqQioiIiIiIZIiZ1QFfBma4+7Fmtguwi7tfnXJoki0/Ap4E/unubmYzgKaUYxqMvgM8CLQDt7v7fAAzOwBYmGZgvfQDYDwwFzgdaCb87OyXZlAiWaMEqYiIiIiISLZcBCwmdCgHWARcDihBKhu4+y+AX5TsWgC8OaVwBi13v8LMbgMmAKVd6xcSGmNl3aHA64D7Adx9hZllfuaryEBTglRERERERCRb9nT3DyedyXH3FjNT/wjZSDLT+EuEmcYfInS13wUl0vuduy8BlnTb93JK4fRVq7vHZgZA8rdEpRhEutF/siIiIiIiItnSVrqRzPbSezfp7iKgEtg72V7ExvUxRQAeMbMPAVFSf/Qi4LZ0QxLJHv0nKyIiIiIiki23mtmXgWozmwf8Abgm3ZAkg/Z09zMItTFx9xb0Hl829XlCc6mJwN2En5EvphmQSBZpib2IiIiIiEi2fIWQwGgGzgOuBc5NNSLJIs00lt6I3P1jlNRLNbNhKcYjkklKkIqIiIiIiGSIu3cA30o+RDan+0zjz6OZxrKpmwkd7Le2T2RIU4JUREREREQkQ8zsvJ72u7uWxUopzTSWzTKzCqAKyJlZLa82ZhoO1KUWmEhGKUEqIiIiIiKSLWtLvq4B3gF4SrFIxpjZ9939VOA97q6ZxrI5XyE07YrZ+G/KGuD7qUQkkmFRHMdpxyAiIiIiIiKbYWaNwJXu/ta0Y5H0mdmD7r63md3v7lomLVtkZue7+ylpxyGSdUqQioiIiIiIZJiZRcB8d9817VgkfWb2V2AqMB2Y3/24u+834EGJiJQ5LbEXERERERHJkG41SHPAPsDjKYUj2fMeQoOdS4HTUo5FMsrM/unuh5nZMsIy+y4RELv7uJRCE8kkJUhFRERERESypbReYCfwM+CqlGKRjHH3DuBuM3u7uz+VdjySWcclny3VKETKhJbYi4iIiIiIiJQZM6sDzgTenOz6B/BNd1+XXlQiIuVJM0hFREREREQyoNvS+k24+xcHKhYpCz8lvKf/bLL9UeB84KTUIpLMMbMDgfOAGYSfFy2xF+mBEqQiIiIiIiLZsLaHfTEhoSHS3b7uvmfXhpndATyUYjySTf8HnAPcBRRSjkUks5QgFRERERERyYZvAUcBq9z9RjP7DHA48CTwjVQjkyyKzKze3bsS63UomS6bWu/uv007CJGsU4JUREREREQkGy4Adgeqzex5oBb4C/BGQqOmY1KMTbLnUuBOM/tdsv1B4JIU45Fsus7M3ubuf0s7EJEsU5MmERERERGRDDCz+cAcwkzAxcAYd283sxzwsLvvnmqAkjlmdgSvNmm60d2vTzMeyQ4zW8arJTpGA81AG6pBKtIjzSAVERERERHJhjZ3j4G1Zvasu7cDuHvRzNpTjk0yxMzywL3uPhdQUlR6cjpwE1BMOxCRcqAEqYiIiIiISDZUm9muhBlepV8D1KQXlmSNuxfMrMXMaty9Ne14JJOOAv4bWAPcTEiW3uzuC9IMSiSrtMReREREREQkA8zsBcKS2J7E7j5jAMORjDOz/wH2Aq4EWrr2u/uFqQUlmZKU55hLqGM8DzgIWA3c5O7/kWJoIpmjBKmIiIiIiIhIGTGzUYSGTO1AU8mh2N1PSicqyTIz2w04DDgFmOTujSmHJJIpWmIvIiIiIiIiUibM7IPALwlNd6qB97n7P9ONSrImKdExDzgU2Bt4CrgVOBG4N7XARDJKCVIRERERERGR8vEV4EB3f9DMDgXOApQgle4eA+4CzgGuTxrAichm5NIOQERERERERER6rejuDwK4+03A8JTjkWx6F3A7cDbwsJldZGb/z8wmpBuWSDapBqmIiIiIiIhImTCz+cD7gCjZdWXptrvPTyk0ySgzawAOJjRrei+hVu0u6UYlki1aYi8iIiIiIiJSPuqA67rt69qOgRkDG45kmZmNJdQinUeoR7oDcHeKIYlkkmaQioiIiIiIiIgMImZ2IWHG6HRCU6abgZuAO929LcXQRDJJM0hFRERERERERAaXFcCngTvcvTXtYESyTjNIRUREREREREREZMhSF3sREREREREREREZspQgFRERERERERERkSFLCVIRERERERGRfmZmJ5pZS9pxiIjI1qlJk4iIiIiIiAw4M5sMnAUcCYwDlgHXAV9390VpxtZXZhYDH3D3K0t2/57wekREJOM0g1REREREREQGlJlNBxzYHfgwMBM4DpgD3Gtm09KLrn+4+3p3X5p2HCIisnXqYi8iIiIiIiIDysyuA/YCZrn7upL9dcDTwIPu/nYzi4DPA/8JTCXMMv2Nu38pOX8S8D3gCKAWeAr4nLvfZGZnA+93991Lnv9E4Hx3b0i2zwbeD/wI+BowFvg78FF3X56csy/wLWAuUAU8DJzm7ncmx18Adix5eQvcfVr375Wc+3HgtOS1LAS+6+7/U3I8Bj4OHE6YWfsK8DV3v7RvV1hERPpCM0hFRERERERkwJjZKEJC84LS5ChAsn0h8DYzGwl8GzgT+A5hdukHgBeT56kHbgGmAe8B9gC+sQ0hTSPMXn038GZgFnBxyfFG4DfAG4D9gAeB68xsdHJ83+Tzx4CJJdvdX/dRwPmEZOzuwI+BC83snd1O/RpwDSGB/HvgYjObug2vS0REekk1SEVERERERGQgzQIi4PHNHJ+fHN8D+BzwWXfvSlg+A9yZfH0sMAF4fddsT+DZbYinFjjB3RfChlmet5nZLHd/2t3/VXqymX0aeB/wNuBSd19mZgCr3X3JFr7PFwizX89Ptp8ys32A04E/l5z3m64Zo2Z2JvAZ4BBAs0hFRLYTJUhFREREREQki1qBauCfmzn+OuDhkuTotnqpKzmauBsoArsCT5vZOOAc4FBgPJAnJFX7OqtzVzaemQpwO/Cubvse7vrC3TvNbBmhiZWIiGwnWmIvIiIiIiIiA+kZIAZ228zx3ZLjr1WRMBO1VOU2PM+vCcvmPwccCOwNLCLUI+0P3V9rRw/H9d5dRGQ70h9ZERERERERGTDuvoLQCOmTSVOmDZLtTwF/IyzBbwMO28xTPQDsaWZjNnN8GTA+afTUZe8ezptsZjuUbO9HeK/cVQLgYOCn7v5Xd38MaCbUGi3VQZhZuiWPAwd123cwoaSAiIikSEvsRUREREREZKCdAtwB3GhmXyV0rt+J0C0+Ak5x92Yz+zHwHTNrA24FRgP7uPtFwG+BM4BrzOwM4CVC86Nmd78JuBkYBXzZzH4HzCN0rO9uPfBrM/s8Yen8z4C/uvvTyfGngOPM7G6gHjgPaO/2HC8Ah5nZLUCbu6/q4ft8D7jCzO4DbiA0qvoQ8N7eXTIREdleNINUREREREREBpS7PwsY8BihQ/xzhITn48C+7v58cuqXgO8SOtk/DvwRmJI8x1rgjYTl7n8GHgW+TrJk3d0fBz4BnEyo63k48O0ewnkB+F3yHP9KYvlIyfGTgAbgvuS8i5PHlDqVUKP0RcLM1p5e89XApwlL9ecTmi990t3/3NP5IiIycKI47o/SLiIiIiIiIiLlxczOBt7v7runHYuIiKRHM0hFRERERERERERkyFKCVERERERERERERIYsLbEXERERERERERGRIUszSEVERERERERERGTIUoJUREREREREREREhiwlSEVERERERERERGTIUoJUREREREREREREhiwlSEVERERERERERGTI+v8BYa7DQj31lwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1584x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dependence(['Marital Status', 'Age', 'Hours per week', 'Occupation'], \n",
    "                interventional_shap_values, \n",
    "                X_test, \n",
    "                display_features=X_display, \n",
    "                nrows=2,\n",
    "                ncols=2,\n",
    "                figsize=(22, 10),\n",
    "                alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 3: Decision plots of the variables `Marital Status`, `Age`,  `Sex`, `Race`, `Occupation`, `Education` using the interventional perturbation Tree SHAP algorithm for the test set\n",
    "<a id='figure_10'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By changing, value of `feature` below, one can recolour the decision plots according to the interactions estimate from the path-dependent perturbation example. Generally, the same interaction patterns are observed, with the exception of `Age`, where the interaction with the `Capital Gain` feature is not conclusive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_dep_interactions = {\n",
    "    'Marital Status': 'Hours per week',\n",
    "    'Age': 'Capital Gain',\n",
    "    'Hours per week': 'Age',\n",
    "    'Occupation': 'Sex',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = 'Occupation'\n",
    "x_jitter = 0.5 if feature in ['Occupation', 'Marital Status'] else 0\n",
    "shap.dependence_plot(feature, \n",
    "                     interventional_shap_values,\n",
    "                     X_test,\n",
    "                     feature_names=feature_names,\n",
    "                     display_features=X_display,\n",
    "                     interaction_index=path_dep_interactions[feature],\n",
    "                     alpha=0.5,\n",
    "                     x_jitter=x_jitter\n",
    "                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If interaction effects are of interest, these can be computed exactly using the path-dependent perturbation algorithm as opposed to approximated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### White-box vs black-box model explanations: a comparison with Kernel SHAP \n",
    "<a id='convergence'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main drawback of model-agnostic methods such as [Kernel SHAP](https://docs.seldon.io/projects/alibi/en/stable/methods/KernelSHAP.html) is their sample complexity, which leads to variability in the results obtained. Given enough samples, the feature attributions estimated Kernel SHAP algorithm approach their exact values and give rise to the same feature importance rankings, as shown below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, both the Tree SHAP and Kernel SHAP algorithms are used to explain `100` instances from the test set using a background  dataset of `100` samples (recall the background dataset size limitation of interventional TreeShap). For the Kernel SHAP algorithm, each explanation is computed `10` times to account for the variability in the estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_background_samples = 100  # same background dataset size limitation of interventional TreeShap \n",
    "n_explained = 100\n",
    "background_dataset, y_background = resample(X_train, y_train, n_samples=n_background_samples, replace=False, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_display_background = decode_data(background_dataset)\n",
    "X_explain = X_test[:n_explained, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Predictor returned a scalar value. Ensure the output represents a probability or decision score as opposed to a classification label!\n"
     ]
    }
   ],
   "source": [
    "tree_explainer = TreeShap(model, model_output='raw', task='classification')\n",
    "tree_explainer.fit(background_dataset)\n",
    "explanation = tree_explainer.explain(X_explain)\n",
    "tree_shap_values = explanation.shap_values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`xgboost` requires the model inputs to be a `DMatrix` instance, so `predict_fcn` needs to account for this transformation to avoid errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_fcn = lambda x: model.predict(xgb.DMatrix(x, feature_names=feature_names))\n",
    "kernel_explainer = KernelShap(predict_fcn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Predictor returned a scalar value. Ensure the output represents a probability or decision score as opposed to a classification label!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KernelShap(meta={\n",
       "  'name': 'KernelShap',\n",
       "  'type': ['blackbox'],\n",
       "  'task': 'classification',\n",
       "  'explanations': ['local', 'global'],\n",
       "  'params': {\n",
       "              'link': 'identity',\n",
       "              'group_names': None,\n",
       "              'grouped': False,\n",
       "              'groups': None,\n",
       "              'weights': None,\n",
       "              'summarise_background': False,\n",
       "              'summarise_result': None,\n",
       "              'transpose': False,\n",
       "              'kwargs': {}}\n",
       "            ,\n",
       "  'version': '0.7.1dev'}\n",
       ")"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel_explainer.fit(background_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To assess convergence, Kernel SHAP is run with the numbers of samples specified in `n_samples` for `n_runs`. Since the computation is quite slow, you can skip the computation and can **load the precomputed results** by setting `COMPUTE_SHAP = False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_runs = 5\n",
    "# There is no point going beyond 2^(num_features) = 2^12 since larger number will be truncated to 2^(num_features).\n",
    "# 2^(num_features) represents the maximum number of enumerable subsets of the set of input features.\n",
    "n_samples = [64, 128, 512, 1024, 4096]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "COMPUTE_SHAP = False  # whether to compute the SHAP values from scratch (the computation is quite slow).\n",
    "\n",
    "if COMPUTE_SHAP:\n",
    "    results = defaultdict(list)\n",
    "    times = defaultdict(list)\n",
    "\n",
    "    for n_samp in n_samples:\n",
    "        print(f\"Number of samples {n_samp}\")\n",
    "        for run in range(n_runs):\n",
    "            t_start = timer()\n",
    "            exp = kernel_explainer.explain(X_explain, nsamples=n_samp, l1_reg=False)\n",
    "            t_end = timer()\n",
    "            times[str(n_samp)].append(t_end - t_start)\n",
    "            results[str(n_samp)].append(exp.shap_values[0])\n",
    "\n",
    "            results['time'] = times\n",
    "\n",
    "            with open('assets/kernel_convergence.pkl', 'wb') as f:\n",
    "                pickle.dump(results, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('assets/kernel_convergence.pkl', 'rb') as f:\n",
    "    convergence_data = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compare the two algorithms, the mean absolute deviation from the ground truth provided by the Tree SHAP algorithm with interventional feature perturbation is computed. For each number of samples, either the maximum mean absolute deviation across the feature, or the mean of this quantity across the features is computed. This calculation can be performed for one instance, or averaged across an entire distribution. The plots below show that all these quantities approach to the ground truth values. A threshold of $1\\%$ from the effect of the most important feature (`Marital Status`) is depicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_errors(tree_shap_values, convergence_data, instance_idx=None):\n",
    "    \"\"\"\n",
    "    Compute the mean and max maximum absolute deviation of Kernel SHAP values\n",
    "    from Tree SHAP values for a specific instance or as an average over instances.\n",
    "    If instance_idx is set, then the errors are computed at instance level.\n",
    "    \"\"\"\n",
    "    \n",
    "    mad = []\n",
    "    for key in convergence_data:\n",
    "        if key != 'time':\n",
    "            mad.append(np.abs(tree_shap_values - np.mean(convergence_data[key], axis=0)))\n",
    "    \n",
    "    if instance_idx is not None:\n",
    "        err_max = [max(x[instance_idx, :]) for x in mad]\n",
    "        err_mean =[np.mean(x[instance_idx, :]).item() for x in mad]\n",
    "    else:\n",
    "        err_max = [max(x.mean(axis=0)) for x in mad]\n",
    "        err_mean =[np.mean(x.mean(axis=0)).item() for x in mad]\n",
    "    \n",
    "    return err_max, err_mean \n",
    "\n",
    "\n",
    "def plot_convergence(err_mean, err_max, n_samples, threshold, instance_idx=None):\n",
    "    \"\"\"\n",
    "    Plots the average error across the features and the maximum error across \n",
    "    features as a function of the number of samples Kernel SHAP uses to estimate\n",
    "    the contributions. \n",
    "    \"\"\"\n",
    "    \n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "    \n",
    "    ax1.loglog(n_samples, err_max, '--*')\n",
    "    ax1.plot([0] + n_samples, [threshold]*(len(n_samples)+1), '--', color='gray', linewidth='3')\n",
    "    ax1.grid(True)\n",
    "    ax1.set_ylabel('Estimation error (max over all features)')\n",
    "    ax1.set_xlabel('Number of samples')\n",
    "    \n",
    "    ax2.loglog(n_samples, err_mean, '--*')\n",
    "    ax2.plot([0] + n_samples, [threshold]*(len(n_samples)+1), '--', color='gray', linewidth='3')\n",
    "    ax2.grid(True)\n",
    "    ax2.set_ylabel('Estimation error (mean over all features)')\n",
    "    ax2.set_xlabel('Number of samples')\n",
    "    if instance_idx is not None:\n",
    "        plt.suptitle(f'Convergence of the Kernel SHAP algorithm to exact shap values (instance {instance_idx})')\n",
    "    else:\n",
    "        plt.suptitle('Convergence of the Kernel SHAP algorithm to exact shap values (mean)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0.01 * np.max(np.mean(np.abs(tree_shap_values), axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "err_max, err_mean = get_errors(tree_shap_values, convergence_data, instance_idx=0)\n",
    "plot_convergence(err_max, err_mean, n_samples, threshold, instance_idx=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 4: Converge of Kernel SHAP to true values according to the maximum error (left) and mean error (right) for instance 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "e_err_max, e_err_mean = get_errors(tree_shap_values, convergence_data)\n",
    "plot_convergence(e_err_max, e_err_mean, n_samples, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 5: Converge of Kernel SHAP according to the maximum error (left) and mean error (right) averaged across 100 instances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a high enough number of samples is selected, the algorithms yield the same global patterns, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "74eab7d50e0f462990a720fa9559388e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_explained = 500\n",
    "X_explained = X_test[:n_explained, :]\n",
    "explanation_500_kernel = kernel_explainer.explain(X_explained, nsamples=1024, l1_reg=False)\n",
    "shap_values_500_kernel = explanation_500_kernel.shap_values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "explanation_500_tree = tree_explainer.explain(X_explained)\n",
    "shap_values_500_tree = explanation_500_tree.shap_values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we can observe that the local accuracy holds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0.0: 500})\n"
     ]
    }
   ],
   "source": [
    "errs = np.round(np.abs(model.predict(xgb.DMatrix(X_explained, feature_names=feature_names)) - tree_explainer.expected_value - shap_values_500_tree.sum(1)), 2)\n",
    "print(Counter(errs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values_500_tree, X_explained, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the Tree SHAP values take a few seconds to compute, the Kernel SHAP takes a few minutes to provide estimates for the shap values. Note that this is also a consequence of the fact that the implementation of Tree SHAP is distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values_500_tree, X_explained, feature_names, plot_type='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 6: Feature importances estimated using the interventional feature perturbation Tree SHAP algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x453.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values_500_kernel, X_explained, feature_names, plot_type='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 7: Feature importances estimated using the Kernel SHAP algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max absolute deviation from ground truth: 0.0443.\n",
      "Min absolute deviation from ground truth: 0.0.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Max absolute deviation from ground truth: {np.round(np.max(np.abs(shap_values_500_tree - shap_values_500_kernel)), 4)}.\")\n",
    "print(f\"Min absolute deviation from ground truth: {np.round(np.min(np.abs(shap_values_500_tree - shap_values_500_kernel)), 4)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the errors incurred in estimating the shap values are relatively small, the feature importance rankings shown in Figures 6 and 7 are identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average prediction on background data is the expected value of kernel explainer: True\n",
      "Average expected value for kernel explainer is the same as the tree explainer: True\n"
     ]
    }
   ],
   "source": [
    "average_prediction = model.predict(xgb.DMatrix(background_dataset, feature_names=feature_names)).mean()\n",
    "kernel_exp_value = kernel_explainer.expected_value\n",
    "tree_exp_value = tree_explainer.expected_value\n",
    "print(f\"Average prediction on background data is the expected value of kernel explainer: {np.abs(average_prediction - kernel_exp_value) < 1e-3}\")\n",
    "print(f\"Average expected value for kernel explainer is the same as the tree explainer: {np.abs(kernel_exp_value - tree_exp_value) < 1e-3}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The expected values of the two explainers are approximately the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The difference between the expected values is 0.0.\n"
     ]
    }
   ],
   "source": [
    "print(f\"The difference between the expected values is {np.round(np.abs(kernel_exp_value - tree_exp_value),2)}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='References'></a>\n",
    "\n",
    "[[1]](#source_3) Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N. and Lee, S.I., 2020. From local explanations to global understanding with explainable AI for trees. Nature machine intelligence, 2(1), pp.56-67."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
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